0–9

• 1.96
  1.96
  1.96 is the approximate value of the 97.5 percentile point of the normal distribution used in probability and statistics. 95% of the area under a normal curve lies within roughly 1.96 standard deviations of the mean, and due to the central limit theorem, this number is therefore used in the...
  
  
• 2SLS (two-stage least squares) — redirects to instrumental variable
  Instrumental variable
  In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables is used to estimate causal relationships when controlled experiments are not feasible....
  
  
• 3SLS — redirects to Three-stage least squares
• 68-95-99.7 rule
  68-95-99.7 rule
  In statistics, the 68-95-99.7 rule, or three-sigma rule, or empirical rule, states that for a normal distribution, nearly all values lie within 3 standard deviations of the mean....
  
  
• 100-year flood
  100-year flood
  A one-hundred-year flood is calculated to be the level of flood water expected to be equaled or exceeded every 100 years on average. The 100-year flood is more accurately referred to as the 1% annual exceedance probability flood, since it is a flood that has a 1% chance of being equaled or exceeded...
  
  

A

• A posteriori probability (disambiguation)
• A priori probability
  A priori probability
  The term a priori probability is used in distinguishing the ways in which values for probabilities can be obtained. In particular, an "a priori probability" is derived purely by deductive reasoning...
  
  
• A priori (statistics)
  A priori (statistics)
  In statistics, a priori knowledge is prior knowledge about a population, rather than that estimated by recent observation. It is common in Bayesian inference to make inferences conditional upon this knowledge, and the integration of a priori knowledge is the central difference between the Bayesian...
  
  
• Abductive reasoning
  Abductive reasoning
  Abduction is a kind of logical inference described by Charles Sanders Peirce as "guessing". The term refers to the process of arriving at an explanatory hypothesis. Peirce said that to abduce a hypothetical explanation a from an observed surprising circumstance b is to surmise that a may be true...
  
  
• Absolute deviation
  Absolute deviation
  In statistics, the absolute deviation of an element of a data set is the absolute difference between that element and a given point. Typically the point from which the deviation is measured is a measure of central tendency, most often the median or sometimes the mean of the data set.D_i = |x_i-m|...
  
  
• Absolute risk reduction
  Absolute risk reduction
  In epidemiology, the absolute risk reduction or risk difference is the decrease in risk of a given activity or treatment in relation to a control activity or treatment. It is the inverse of the number needed to treat....
  
  
• ABX test
  ABX test
  An ABX test is a method of comparing two kinds of sensory stimuli to identify detectable differences. A subject is presented with two known samples , and one unknown sample X, for three samples total. X is randomly selected from A and B, and the subject identifies X as being either A or B...
  
  
• Accelerated failure time model
  Accelerated failure time model
  In the statistical area of survival analysis, an accelerated failure time model is a parametric model that provides an alternative to the commonly used proportional hazards models...
  
  
• Acceptable quality limit
  Acceptable quality limit
  The acceptable quality limit is the worst tolerable process average in percentage or ratio, that is still considered acceptable: that is, it is at an acceptable quality level...
  
  
• Acceptance sampling
  Acceptance sampling
  Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry and particularly the military for contracts and procurement. It is usually done as products leave the factory, or in...
  
  
• Accidental sampling
  Accidental sampling
  Accidental sampling is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient...
  
  
• Accuracy and precision
  Accuracy and precision
  In the fields of science, engineering, industry and statistics, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's actual value. The precision of a measurement system, also called reproducibility or repeatability, is the degree to which...
  
  
• Accuracy paradox
  Accuracy paradox
  The accuracy paradox for predictive analytics states that predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy...
  
  
• Acquiescence bias
  Acquiescence bias
  Acquiescence bias is a category of response bias in which respondents to a survey have a tendency to agree with all the questions or to indicate a positive connotation. Acquiescence is sometimes referred to as "yah-saying" and is the tendency of a respondent to agree with a statement when in doubt...
  
  
• Actuarial science
  Actuarial science
  Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries. Actuaries are professionals who are qualified in this field through education and experience...
  
  
• ADAPA
  ADAPA
  ADAPA is intrinsically a predictive decisioning platform. It combines the power of predictive analytics and business rules to facilitate the tasks of managing and designing automated decisioning systems.-Automated decisions:...
  
   – software
• Adapted process
  Adapted process
  In the study of stochastic processes, an adapted process is one that cannot "see into the future". An informal interpretation is that X is adapted if and only if, for every realisation and every n, Xn is known at time n...
  
  
• Adaptive estimator
  Adaptive estimator
  In statistics, an adaptive estimator is an estimator in a parametric or semiparametric model with nuisance parameters such that the presence of these nuisance parameters does not affect efficiency of estimation.-Definition:...
  
  
• Additive Markov chain
  Additive Markov chain
  In probability theory, an additive Markov chain is a Markov chain with an additive conditional probability function. Here the process is a discrete-time Markov chain of order m and the transition probability to a state at the next time is a sum of functions, each depending on the next state and one...
  
  
• Additive model
  Additive model
  In statistics, an additive model is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle and is an essential part of the ACE algorithm. The AM uses a one dimensional smoother to build a restricted class of nonparametric regression models. Because of this,...
  
  
• Additive smoothing
  Additive smoothing
  In statistics, additive smoothing, also called Laplace smoothing , or Lidstone smoothing, is a technique used to smooth categorical data...
  
  
• Additive white Gaussian noise
  Additive white Gaussian noise
  Additive white Gaussian noise is a channel model in which the only impairment to communication is a linear addition of wideband or white noise with a constant spectral density and a Gaussian distribution of amplitude. The model does not account for fading, frequency selectivity, interference,...
  
  
• Adjusted Rand index — redirects to Rand index
  Rand index
  The Rand index or Rand measure in statistics, and in particular in data clustering, is a measure of the similarity between two data clusterings...
  
   (subsection)
• ADMB
  ADMB
  ADMB or AD Model Builder is a free and open source software suite for non-linear statistical modeling. It was created by David Fournier and now being developed by the ADMB Project, a creation of the non-profit ADMB Foundation...
  
   – software
• Admissible decision rule
  Admissible decision rule
  In statistical decision theory, an admissible decision rule is a rule for making a decision such that there isn't any other rule that is always "better" than it, in a specific sense defined below....
  
  
• Age adjustment
  Age adjustment
  In epidemiology and demography, age adjustment, also called age standardisation, is a technique used to better allow populations to be compared when the age profiles of the populations are quite different....
  
  
• Age-standardized mortality rate
• Age stratification
  Age stratification
  In critical sociology, age stratification refers to the hierarchical ranking of people into age groups within a society.Age stratification which is based on an ascribed status is a major source inequality, and thus may lead to ageism.-External links:* *...
  
  
• Aggregate data
  Aggregate data
  In statistics, aggregate data describes data combined from several measurements.In economics, aggregate data or data aggregates describes high-level data that is composed of a multitude or combination of other more individual data....
  
  
• Aggregate pattern
  Aggregate pattern
  An Aggregate pattern can refer to concepts in either statistics or computer programming. Both uses deal with considering a large case as composed of smaller, simpler, pieces.- Statistics :...
  
  
• Akaike information criterion
  Akaike information criterion
  The Akaike information criterion is a measure of the relative goodness of fit of a statistical model. It was developed by Hirotsugu Akaike, under the name of "an information criterion" , and was first published by Akaike in 1974...
  
  
• Algebra of random variables
  Algebra of random variables
  In the algebraic axiomatization of probability theory, the primary concept is not that of probability of an event, but rather that of a random variable. Probability distributions are determined by assigning an expectation to each random variable...
  
  
• Algebraic statistics
  Algebraic statistics
  Algebraic statistics is the use of algebra to advance statistics. Algebra has been useful for experimental design, parameter estimation, and hypothesis testing....
  
  
• Algorithmic inference
  Algorithmic inference
  Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst...
  
  
• Algorithms for calculating variance
  Algorithms for calculating variance
  Algorithms for calculating variance play a major role in statistical computing. A key problem in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with...
  
  
• All-pairs testing
  All-pairs testing
  All-pairs testing or pairwise testing is a combinatorial software testing method that, for each pair of input parameters to a system , tests all possible discrete combinations of those parameters...
  
  
• Allan variance
  Allan variance
  The Allan variance , also known as two-sample variance, is a measure of frequency stability in clocks, oscillators and amplifiers. It is named after David W. Allan. It is expressed mathematically as\sigma_y^2. \,...
  
  
• Alignments of random points
  Alignments of random points
  Alignments of random points, as shown by statistics, can be found when a large number of random points are marked on a bounded flat surface. This might be used to show that ley lines exist due to chance alone .One precise definition which expresses the generally accepted meaning of "alignment"...
  
  
• Almost surely
  Almost surely
  In probability theory, one says that an event happens almost surely if it happens with probability one. The concept is analogous to the concept of "almost everywhere" in measure theory...
  
  
• Alpha beta filter
  Alpha beta filter
  An alpha beta filter is a simplified form of observer for estimation, data smoothing and control applications. It is closely related to Kalman filters and to linear state observers used in control theory...
  
  
• Alternative hypothesis
• Analyse-it
  Analyse-it
  Analyse-it is a statistical analysis add-in for Microsoft Excel. Analyse-it is the successor to Astute, developed in 1992 for Excel 4 and the first statistical analysis add-in for Microsoft Excel...
  
   – software
• Analysis of categorical data
  Analysis of categorical data
  This a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables* Categorical distribution, general model* Stratified analysis* Chi-squared test...
  
  
• Analysis of covariance
• Analysis of molecular variance
  Analysis of molecular variance
  Analysis of molecular variance , is a statistical model for the molecular variation in a single species, typically biological. The name and model are inspired by ANOVA. The method was developed by Laurent Excoffier, Peter Smouse and Joseph Quattro at Rutgers University in 1992.Since developing...
  
  
• Analysis of rhythmic variance
  Analysis of rhythmic variance
  In statistics, analysis of rhythmic variance is a method for detecting rhythms in biological time series, published by Peter Celec . It is a procedure for detecting cyclic variations in biological time series and quantification of their probability...
  
  
• Analysis of variance
  Analysis of variance
  In statistics, analysis of variance is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation...
  
  
• Analytic and enumerative statistical studies
  Analytic and enumerative statistical studies
  Analytic and enumerative statistical studies are two types of scientific studies:In any statistical study the ultimate aim is to provide a rational basis for action. Enumerative and analytic studies differ by where the action is taken...
  
  
• Ancestral graph
  Ancestral graph
  An ancestral graph is a graph with three types of edges: directed edge, bidirected edge, and undirected edge such that it can be decomposed into three parts: an undirected subgraph, a directed subgraph, and directed edges pointing from the undirected subgraph to the directed subgraph.An ancestral...
  
  
• Anchor test
  Anchor test
  In psychometrics, an anchor test is a common set of test items administered in combination with two or more alternative forms of the test with the aim of establishing the equivalence of the test scores on the alternative forms. The purpose of the anchor test is to provide a baseline for an...
  
  
• Ancillary statistic
  Ancillary statistic
  In statistics, an ancillary statistic is a statistic whose sampling distribution does not depend on which of the probability distributions among those being considered is the distribution of the statistical population from which the data were taken...
  
  
• ANCOVA
  ANCOVA
  In statistics, analysis of covariance is a general linear model with a continuous outcome variable and two or more predictor variables where at least one is continuous and at least one is categorical . ANCOVA is a merger of ANOVA and regression for continuous variables...
  
   – redirects to Analysis of covariance
• Anderson–Darling test
• ANOVA
• ANOVA on ranks
  ANOVA on ranks
  In statistics, one purpose for the analysis of variance is to analyze differences in means between groups. The test statistic, F, assumes independence of observations, homogeneous variances, and population normality...
  
  
• ANOVA-simultaneous component analysis
  ANOVA-simultaneous component analysis
  ASCA, ANOVA-SCA, or analysis of variance – simultaneous component analysis is a method that partitions variation and enables interpretation of these partitions by SCA, a method that is similar to PCA. This method is a multi or even megavariate extension of ANOVA. The variation partitioning is...
  
  
• Anomaly detection
  Anomaly detection
  Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior....
  
  
• Anomaly time series
  Anomaly time series
  In atmospheric sciences and some other applications of statistics, an anomaly time series is the time series of deviations of a quantity from some mean. Similarly a standardized anomaly series contains values of deviations divided by a standard deviation...
  
  
• Anscombe transform
  Anscombe transform
  In statistics, the Anscombe transform, named after Francis Anscombe, is a variance-stabilizing transformation that transforms a random variable with a Poisson distribution into one with an approximately standard Gaussian distribution. The Anscombe transform is widely used in photon-limited imaging ...
  
  
• Anscombe's quartet
  Anscombe's quartet
  Anscombe's quartet comprises four datasets that have identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven points. They were constructed in 1973 by the statistician F.J...
  
  
• Antecedent variable
  Antecedent variable
  In statistics and social sciences, an antecedent variable is a variable that can help to explain the apparent relationship between other variables that are nominally in a cause and effect relationship...
  
  
• Antithetic variates
  Antithetic variates
  The antithetic variates method is a variance reduction technique used in Monte Carlo methods. Considering that the error reduction in the simulated signal has a square root convergence , a very large number of sample paths is required to obtain an accurate result.-Underlying principle:The...
  
  
• Approximate Bayesian computation
  Approximate Bayesian computation
  Approximate Bayesian computation is a family of computational techniques in Bayesian statistics. These simulation techniques operate on summary data to make broad inferences with less computation than might be required if all available data were analyzed in detail...
  
  
• Arcsine distribution
• Area chart
  Area chart
  An area chart or area graph displays graphically quantitive data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings...
  
  
• Area compatibility factor
• ARGUS distribution
• Arithmetic mean
  Arithmetic mean
  In mathematics and statistics, the arithmetic mean, often referred to as simply the mean or average when the context is clear, is a method to derive the central tendency of a sample space...
  
  
• Armitage–Doll multistage model of carcinogenesis
• Arrival theorem
• Artificial neural network
  Artificial neural network
  An artificial neural network , usually called neural network , is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes...
  
  
• Ascertainment bias
• ASReml
  ASReml
  ASReml is a statistical software package for fitting linear mixed models using restricted maximum likelihood, a technique commonly used in plant and animal breeding and quantitative genetics as well as other fields...
  
    – software
• Association (statistics)
  Association (statistics)
  In statistics, an association is any relationship between two measured quantities that renders them statistically dependent. The term "association" refers broadly to any such relationship, whereas the narrower term "correlation" refers to a linear relationship between two quantities.There are many...
  
  
• Association mapping
  Association mapping
  Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci that takes advantage of historic linkage disequilibrium to link phenotypes to genotypes .-Theory:Association mapping is based on the idea that traits that have entered a population...
  
  
• Association scheme
  Association scheme
  The theory of association schemes arose in statistics, in the theory of experimental design for the analysis of variance. In mathematics, association schemes belong to both algebra and combinatorics. Indeed, in algebraic combinatorics, association schemes provide a unified approach to many topics,...
  
  
• Assumed mean
  Assumed mean
  In statistics the assumed mean is a method for calculating the arithmetic mean and standard deviation of a data set. It simplifies calculating accurate values by hand. Its interest today is chiefly historical but it can be used to quickly estimate these statistics...
  
  
• Asymptotic distribution
  Asymptotic distribution
  In mathematics and statistics, an asymptotic distribution is a hypothetical distribution that is in a sense the "limiting" distribution of a sequence of distributions...
  
  
• Asymptotic equipartition property
  Asymptotic equipartition property
  In information theory the asymptotic equipartition property is a general property of the output samples of a stochastic source. It is fundamental to the concept of typical set used in theories of compression....
  
   (information theory)
• Asymptotic normality – redirects to Asymptotic distribution
  Asymptotic distribution
  In mathematics and statistics, an asymptotic distribution is a hypothetical distribution that is in a sense the "limiting" distribution of a sequence of distributions...
  
  
• Asymptotic relative efficiency redirects to Efficiency (statistics)
  Efficiency (statistics)
  In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some “best possible” manner. The notion of “best possible” relies upon the choice of a particular loss function — the function which quantifies the relative degree of undesirability of estimation errors...
  
  
• Asymptotic theory (statistics)
  Asymptotic theory (statistics)
  In statistics, asymptotic theory, or large sample theory, is a generic framework for assessment of properties of estimators and statistical tests...
  
  
• Atkinson index
  Atkinson index
  The Atkinson index is a measure of income inequality developed by British economist Anthony Barnes Atkinson...
  
  
• Attack rate
  Attack rate
  In epidemiology, an attack rate is the cumulative incidence of infection in a group of people observed over a period of time during an epidemic, usually in relation to foodborne illness....
  
  
• Augmented Dickey–Fuller test
• Aumann's agreement theorem
  Aumann's agreement theorem
  Aumann's agreement theorem says that two people acting rationally and with common knowledge of each other's beliefs cannot agree to disagree...
  
  
• Autocorrelation
  Autocorrelation
  Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time separation between them...
  
  
  • Autocorrelation plot redirects to Correlogram
    Correlogram
    In the analysis of data, a correlogram is an image of correlation statistics. For example, in time series analysis, a correlogram, also known as an autocorrelation plot, is a plot of the sample autocorrelations r_h\, versus h\, ....
    
    
• Autocovariance
  Autocovariance
  In statistics, given a real stochastic process X, the autocovariance is the covariance of the variable with itself, i.e. the variance of the variable against a time-shifted version of itself...
  
  
• Autoregressive conditional duration
  Autoregressive conditional duration
  In financial econometrics, an autoregressive conditional duration model considers irregularly spaced and autocorrelated intertrade durations. ACD is analogous to GARCH...
  
  
• Autoregressive conditional heteroskedasticity
  Autoregressive conditional heteroskedasticity
  In econometrics, AutoRegressive Conditional Heteroskedasticity models are used to characterize and model observed time series. They are used whenever there is reason to believe that, at any point in a series, the terms will have a characteristic size, or variance...
  
  
• Autoregressive fractionally integrated moving average
  Autoregressive fractionally integrated moving average
  In statistics, autoregressive fractionally integrated moving average models are time series models that generalize ARIMA models by allowing non-integer values of the differencing parameter and are useful in modeling time series with long memory...
  
  
• Autoregressive integrated moving average
  Autoregressive integrated moving average
  In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average model is a generalization of an autoregressive moving average model. These models are fitted to time series data either to better understand the data or to predict future points...
  
  
• Autoregressive model
  Autoregressive model
  In statistics and signal processing, an autoregressive model is a type of random process which is often used to model and predict various types of natural phenomena...
  
  
• Autoregressive moving average model
  Autoregressive moving average model
  In statistics and signal processing, autoregressive–moving-average models, sometimes called Box–Jenkins models after the iterative Box–Jenkins methodology usually used to estimate them, are typically applied to autocorrelated time series data.Given a time series of data Xt, the ARMA model is a...
  
  
• Auxiliary particle filter
  Auxiliary particle filter
  The auxiliary particle filter is a particle filtering algorithm introduced by Pitt and Shephard in 1999 to improve some deficiencies of the sequential importance resampling algorithm when dealing with tailed observation densities....
  
  
• Average
  Average
  In mathematics, an average, or central tendency of a data set is a measure of the "middle" value of the data set. Average is one form of central tendency. Not all central tendencies should be considered definitions of average....
  
  
• Average treatment effect
• Averaged one-dependence estimators
• Azuma's inequality

B

• BA model
  BA model
  The Barabási–Albert model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. Scale-free networks are widely observed in natural and man-made systems, including the Internet, the world wide web, citation networks, and some social...
  
   – model for a random network
• Backfitting algorithm
  Backfitting algorithm
  In statistics, the backfitting algorithm is a simple iterative procedure used to fit a generalized additive model. It was introduced in 1985 by Leo Breiman and Jerome Friedman along with generalized additive models...
  
  
• Balance equation
  Balance equation
  In probability theory, a balance equation is an equation that describes the probability flux associated with a Markov chain in and out of states or set of states.-Global balance:...
  
  
• Balanced incomplete block design redirects to Block design
• Balanced repeated replication
  Balanced repeated replication
  Balanced repeated replication is a statistical technique for estimating the sampling variability of a statistic obtained by stratified sampling.- Outline of the technique :# Select balanced half-samples from the full sample....
  
  
• Balding–Nichols model
• Banburismus
  Banburismus
  Banburismus was a cryptanalytic process developed by Alan Turing at Bletchley Park in England during the Second World War. It was used by Bletchley Park's Hut 8 to help break German Kriegsmarine messages enciphered on Enigma machines. The process used sequential conditional probability to infer...
  
   — related to Bayesian networks
• Bapat–Beg theorem
• Bar chart
  Bar chart
  A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally....
  
  
• Barabási–Albert model
• Barber–Johnson diagram
• Barnard's test
  Barnard's test
  In statistics, Barnard's test is an exact test of the null hypothesis of independence of rows and columns in a contingency table. It is an alternative to Fisher's exact test but is more time-consuming to compute...
  
  
• Barnardisation
  Barnardisation
  Barnardisation is a method of disclosure control for tables of counts that involves randomly adding or subtracting 1 from some cells in the table....
  
  
• Barnes interpolation
  Barnes interpolation
  Barnes interpolation, named after Stanley L. Barnes, is the interpolation of unstructured data points from a set of measurements of an unknown function in two dimensions into an analytic function of two variables...
  
  
• Bartlett's method
• Bartlett's test
  Bartlett's test
  In statistics, Bartlett's test is used to test if k samples are from populations with equal variances. Equal variances across samples is called homoscedasticity or homogeneity of variances. Some statistical tests, for example the analysis of variance, assume that variances are equal across groups...
  
  
• Base rate
  Base rate
  In probability and statistics, base rate generally refers to the class probabilities unconditioned on featural evidence, frequently also known as prior probabilities...
  
  
• Baseball statistics
  Baseball statistics
  Statistics play an important role in summarizing baseball performance and evaluating players in the sport.Since the flow of a baseball game has natural breaks to it, and normally players act individually rather than performing in clusters, the sport lends itself to easy record-keeping and statistics...
  
  
• Basu's theorem
  Basu's theorem
  In statistics, Basu's theorem states that any boundedly complete sufficient statistic is independent of any ancillary statistic. This is a 1955 result of Debabrata Basu....
  
  
• Bates distribution
  Bates distribution
  In probability and statistics, the Bates distribution, is a probability distribution of the mean of a number of statistically independent uniformly distributed random variables on the unit interval...
  
  
• Baum–Welch algorithm
• Bayes' rule
  Bayes' rule
  In probability theory and applications, Bayes' rule relates the odds of event A_1 to event A_2, before and after conditioning on event B. The relationship is expressed in terms of the Bayes factor, \Lambda. Bayes' rule is derived from and closely related to Bayes' theorem...
  
  
• Bayes' theorem
  Bayes' theorem
  In probability theory and applications, Bayes' theorem relates the conditional probabilities P and P. It is commonly used in science and engineering. The theorem is named for Thomas Bayes ....
  
  
  • Evidence under Bayes theorem
    Evidence under Bayes theorem
    Bayes' theorem provides a way of updating the probability of an event in the light of new information. In the evidence law context, for example, it could be used as a way of updating the probability that a genetic sample found at the scene of the crime came from the defendant in light of a genetic...
    
    
• Bayes estimator
  Bayes estimator
  In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function . Equivalently, it maximizes the posterior expectation of a utility function...
  
  
• Bayes factor
  Bayes factor
  In statistics, the use of Bayes factors is a Bayesian alternative to classical hypothesis testing. Bayesian model comparison is a method of model selection based on Bayes factors.-Definition:...
  
  
• Bayes linear statistics
• Bayesian — disambiguation
• Bayesian additive regression kernels
  Bayesian additive regression kernels
  Bayesian additive regression kernels is a non-parametric statistical model for regression and statistical classification.The unknown mean function is represented as a weighted sum of kernel functions, which is constructed by a prior using...
  
  
• Bayesian average
  Bayesian average
  A Bayesian average is a method of estimating the mean of a population consistent with Bayesian interpretation, where instead of estimating the mean strictly from the available data set, other existing information related to that data set may also be incorporated into the calculation in order to...
  
  
• Bayesian brain
  Bayesian brain
  Bayesian brain is a term that is used to refer to the ability of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. This term is used in behavioural sciences and neuroscience and studies associated with this term...
  
  
• Bayesian econometrics
  Bayesian econometrics
  Bayesian econometrics is a branch of econometrics which applies Bayesian principles to economic modelling. Bayesianism is based on a degree-of-belief interpretation of probability, as opposed to a relative-frequency interpretation....
  
  
• Bayesian experimental design
  Bayesian experimental design
  Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment...
  
  
• Bayesian game
  Bayesian game
  In game theory, a Bayesian game is one in which information about characteristics of the other players is incomplete. Following John C. Harsanyi's framework, a Bayesian game can be modelled by introducing Nature as a player in a game...
  
  
• Bayesian inference
  Bayesian inference
  In statistics, Bayesian inference is a method of statistical inference. It is often used in science and engineering to determine model parameters, make predictions about unknown variables, and to perform model selection...
  
  
• Bayesian inference in phylogeny
  Bayesian inference in phylogeny
  Bayesian inference in phylogeny generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likelihood of the data, generated by a multiple alignment. The Bayesian approach has become more popular due...
  
  
• Bayesian information criterion
• Bayesian linear regression
  Bayesian linear regression
  In statistics, Bayesian linear regression is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference...
  
  
• Bayesian model comparison — redirects to Bayes factor
  Bayes factor
  In statistics, the use of Bayes factors is a Bayesian alternative to classical hypothesis testing. Bayesian model comparison is a method of model selection based on Bayes factors.-Definition:...
  
  
• Bayesian multivariate linear regression
• Bayesian network
  Bayesian network
  A Bayesian network, Bayes network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph . For example, a Bayesian network could represent the probabilistic...
  
  
• Bayesian probability
  Bayesian probability
  Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities. The Bayesian interpretation of probability can be seen as an extension of logic that enables reasoning with propositions, whose truth or falsity is...
  
  
• Bayesian search theory
  Bayesian search theory
  Bayesian search theory is the application of Bayesian statistics to the search for lost objects. It has been used several times to find lost sea vessels, for example the USS Scorpion.-Procedure:The usual procedure is as follows:...
  
  
• Bayesian spam filtering
  Bayesian spam filtering
  Bayesian spam filtering is a statistical technique of e-mail filtering. It makes use of a naive Bayes classifier to identify spam e-mail.Bayesian classifiers work by correlating the use of tokens , with spam and non spam e-mails and then using Bayesian inference to calculate a probability that an...
  
  
• Bayesian statistics
  Bayesian statistics
  Bayesian statistics is that subset of the entire field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief or, more specifically, Bayesian probabilities...
  
  
• Bayesian VAR
  Bayesian VAR
  Bayesian Vector Autoregression is a term which indicates that Bayesian methods are used to estimate a vector autoregression . In that respect, the difference with standard VAR models lies on the fact that the model parameters are treated as random variables, and prior probabilities are assigned to...
  
   — Bayesian Vector Autoregression
• BCMP network
  BCMP network
  In queueing theory, a discipline within the mathematical theory of probability, a BCMP network is a class of queueing network for which a product form equilibrium distribution exists. It is named after the authors of the paper where the network was first described: Baskett, Chandy, Muntz and Palacios...
  
   – queueing theory
• Bean machine
  Bean machine
  The bean machine, also known as the quincunx or Galton box, is a device invented by Sir Francis Galton to demonstrate the central limit theorem, in particular that the normal distribution is approximate to the binomial distribution....
  
  
• Behrens–Fisher problem
• Belief propagation
  Belief propagation
  Belief propagation is a message passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It calculates the marginal distribution for each unobserved node, conditional on any observed nodes...
  
  
• Belt transect
  Belt transect
  Belt transects are used in biology to investigate the distribution of organisms in relation to a certain area, such as the seashore or a meadow. It records all the species found between two lines and how far they are for a certain place or area and how many of them there are...
  
  
• Benford's law
  Benford's law
  Benford's law, also called the first-digit law, states that in lists of numbers from many real-life sources of data, the leading digit is distributed in a specific, non-uniform way...
  
  
• Bennett's inequality
  Bennett's inequality
  In probability theory, Bennett's inequality provides an upper bound on the probability that the sum of independent random variables deviates from its expected value by more than any specified amount...
  
  
• Berkson error model
  Berkson error model
  The Berkson error model is a description of random error in measurement. Unlike classical error, Berkson error causes little or no bias in the measurement. It was proposed by Joseph Berkson in a paper entitled Are there two regressions?, published in 1950.An example of Berkson error arises in...
  
  
• Berkson's paradox
  Berkson's paradox
  Berkson's paradox or Berkson's fallacy is a result in conditional probability and statistics which is counter-intuitive for some people, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions...
  
  
• Berlin procedure
  Berlin procedure
  The so-called Berlin procedure is a mathematical procedure for time series decomposition and seasonal adjustment of monthly and quarterly economic time series. The mathematical foundations of the procedure were developed in 1960's at the Technical University of Berlin and the German Institute...
  
  
• Bernoulli distribution
• Bernoulli process
  Bernoulli process
  In probability and statistics, a Bernoulli process is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1. The component Bernoulli variables Xi are identical and independent...
  
  
• Bernoulli sampling
  Bernoulli sampling
  In the theory of finite population sampling, Bernoulli sampling is a sampling process where each element of the population that is sampled is subjected to an independent Bernoulli trial which determines whether the element becomes part of the sample during the drawing of a single sample...
  
  
• Bernoulli scheme
  Bernoulli scheme
  In mathematics, the Bernoulli scheme or Bernoulli shift is a generalization of the Bernoulli process to more than two possible outcomes. Bernoulli schemes are important in the study of dynamical systems, as most such systems exhibit a repellor that is the product of the Cantor set and a smooth...
  
  
• Bernoulli trial
  Bernoulli trial
  In the theory of probability and statistics, a Bernoulli trial is an experiment whose outcome is random and can be either of two possible outcomes, "success" and "failure"....
  
  
• Bernstein inequalities (probability theory)
• Bernstein–von Mises theorem
  Bernstein–von Mises theorem
  In Bayesian inference, the Bernstein–von Mises theorem provides the basis for the important result that the posterior distribution for unknown quantities in any problem is effectively independent of the prior distribution once the amount of information supplied by a sample of data is large...
  
  
• Berry–Esseen theorem
  Berry–Esséen theorem
  The central limit theorem in probability theory and statistics states that under certain circumstances the sample mean, considered as a random quantity, becomes more normally distributed as the sample size is increased...
  
  
• Bertrand's ballot theorem
• Bertrand's box paradox
  Bertrand's box paradox
  Bertrand's box paradox is a classic paradox of elementary probability theory. It was first posed by Joseph Bertrand in his Calcul des probabilités, published in 1889.There are three boxes:# a box containing two gold coins,...
  
  
• Bessel process
  Bessel process
  In mathematics, a Bessel process, named after Friedrich Bessel, is a type of stochastic process. The n-dimensional Bessel process is the real-valued process X given byX_t = \| W_t \|,...
  
  
• Bessel's correction
  Bessel's correction
  In statistics, Bessel's correction, named after Friedrich Bessel, is the use of n − 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance,...
  
  
• Best linear unbiased prediction
  Best linear unbiased prediction
  In statistics, best linear unbiased prediction is used in linear mixed models for the estimation of random effects. BLUP was derived by Charles Roy Henderson in 1950 but the term "best linear unbiased predictor" seems not to have been used until 1962...
  
  
• Beta (finance)
• Beta-binomial distribution
• Beta-binomial model
• Beta distribution
• Beta function – for incomplete beta function
• Beta negative binomial distribution
• Beta prime distribution
• Beverton–Holt model
• Bhatia–Davis inequality
  Bhatia–Davis inequality
  In mathematics, the Bhatia–Davis inequality, named after Rajendra Bhatia and Chandler Davis, is an upper bound on the variance of any bounded probability distribution on the real line....
  
  
• Bhattacharya coefficient redirects to Bhattacharyya distance
  Bhattacharyya distance
  In statistics, the Bhattacharyya distance measures the similarity of two discrete or continuous probability distributions. It is closely related to the Bhattacharyya coefficient which is a measure of the amount of overlap between two statistical samples or populations. Both measures are named after A...
  
  
• Bias (statistics)
  Bias (statistics)
  A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter of interest. The following lists some types of, or aspects of, bias which should not be considered mutually exclusive:...
  
  
• Bias of an estimator
  Bias of an estimator
  In statistics, bias of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased.In ordinary English, the term bias is...
  
  
• Biased random walk (biochemistry)
  Biased random walk (biochemistry)
  In cell biology, a biased random walk enables bacteria to search for food and flee from harm. Bacteria propel themselves with the aid of flagella in a process called chemotaxis, and a typical bacteria trajectory has many characteristics of a random walk. They move forward for a certain distance,...
  
  
• Biased sample – redirects to Sampling bias
• Biclustering
  Biclustering
  Biclustering, co-clustering, or two-mode clustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix....
  
  
• Big O in probability notation
  Big O in probability notation
  The order in probability notation is used in probability theory and statistical theory in direct parallel to the big-O notation which is standard in mathematics...
  
  
• Bienaymé–Chebyshev inequality
  Chebyshev's inequality
  In probability theory, Chebyshev’s inequality guarantees that in any data sample or probability distribution,"nearly all" values are close to the mean — the precise statement being that no more than 1/k2 of the distribution’s values can be more than k standard deviations away from the mean...
  
  
• Bills of Mortality
  Bills of Mortality
  The London Bills of Mortality were the main source of mortality statistics, designed to monitor deaths from the plague from the 17th century-1830s. They were used mainly as a way of warning about plague epidemics....
  
  
• Bimodal distribution
  Bimodal distribution
  In statistics, a bimodal distribution is a continuous probability distribution with two different modes. These appear as distinct peaks in the probability density function, as shown in Figure 1....
  
  
• Binary classification
  Binary classification
  Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. Some typical binary classification tasks are...
  
  
• Bingham distribution
  Bingham distribution
  In statistics, the Bingham distribution, named after Christopher Bingham, is an antipodally symmetric probability distribution on the n-sphere...
  
  
• Binomial distribution
• Binomial proportion confidence interval
  Binomial proportion confidence interval
  In statistics, a binomial proportion confidence interval is a confidence interval for a proportion in a statistical population. It uses the proportion estimated in a statistical sample and allows for sampling error. There are several formulas for a binomial confidence interval, but all of them rely...
  
  
• Binomial regression
  Binomial regression
  In statistics, binomial regression is a technique in which the response is the result of a series of Bernoulli trials, or a series of one of two possible disjoint outcomes...
  
  
• Binomial test
  Binomial test
  In statistics, the binomial test is an exact test of the statistical significance of deviations from a theoretically expected distribution of observations into two categories.-Common use:...
  
  
• Bioinformatics
  Bioinformatics
  Bioinformatics is the application of computer science and information technology to the field of biology and medicine. Bioinformatics deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, software...
  
  
• Biometrics (statistics) — redirects to Biostatistics
  Biostatistics
  Biostatistics is the application of statistics to a wide range of topics in biology...
  
  
• Biostatistics
  Biostatistics
  Biostatistics is the application of statistics to a wide range of topics in biology...
  
  
• Biplot
  Biplot
  Biplots are a type of exploratory graph used in statistics, a generalization of the simple two-variable scatterplot. A biplot allows information on both samples and variables of a data matrix to be displayed graphically. Samples are displayed as points while variables are displayed either as...
  
  
• Birnbaum–Saunders distribution
• Birth-death process
  Birth-death process
  The birth–death process is a special case of continuous-time Markov process where the states represent the current size of a population and where the transitions are limited to births and deaths...
  
  
• Bispectrum
  Bispectrum
  In mathematics, in the area of statistical analysis, the bispectrum is a statistic used to search for nonlinear interactions. The Fourier transform of the second-order cumulant, i.e., the autocorrelation function, is the traditional power spectrum...
  
  
• Bivariate analysis
  Bivariate analysis
  Bivariate analysis is one of the simplest forms of the quantitative analysis. It involves the analysis of two variables , for the purpose of determining the empirical relationship between them...
  
  
• Bivariate von Mises distribution
  Bivariate von Mises distribution
  In probability theory and statistics, the bivariate von Mises distribution is a probability distribution describing values on a torus. It may be thought of as an analogue on the torus of the bivariate normal distribution. The distribution belongs to the field of directional statistics. The general...
  
  
• Black–Scholes
• Bland–Altman plot
• Blind deconvolution
  Blind deconvolution
  In image processing and applied mathematics, blind deconvolution is a deconvolution technique that permits recovery of the target scene from a single or set of "blurred" images in the presence of a poorly determined or unknown point spread function ....
  
  
• Blind experiment
• Block design
• Blocking (statistics)
  Blocking (statistics)
  In the statistical theory of the design of experiments, blocking is the arranging of experimental units in groups that are similar to one another. For example, an experiment is designed to test a new drug on patients. There are two levels of the treatment, drug, and placebo, administered to male...
  
  
• BMDP
  BMDP
  BMDP is a statistical package developed in 1961 at UCLA. Based on the older BIMED program for biomedical applications, it used keyword parameters in the input instead of fixed-format cards, so the letter P was added to the letters BMD, although the name was later defined as being an abbreviation...
  
   – software
• Bochner's theorem
  Bochner's theorem
  In mathematics, Bochner's theorem characterizes the Fourier transform of a positive finite Borel measure on the real line.- Background :...
  
  
• Bonferroni correction
  Bonferroni correction
  In statistics, the Bonferroni correction is a method used to counteract the problem of multiple comparisons. It was developed and introduced by Italian mathematician Carlo Emilio Bonferroni...
  
  
• Bonferroni inequalities – redirects to Boole's inequality
  Boole's inequality
  In probability theory, Boole's inequality, also known as the union bound, says that for any finite or countable set of events, the probability that at least one of the events happens is no greater than the sum of the probabilities of the individual events...
  
  
• Boole's inequality
  Boole's inequality
  In probability theory, Boole's inequality, also known as the union bound, says that for any finite or countable set of events, the probability that at least one of the events happens is no greater than the sum of the probabilities of the individual events...
  
  
• Boolean analysis
  Boolean analysis
  Boolean analysis was introduced by Flament . The goal of a Boolean analysis is to detect deterministic dependencies between the items of a questionnaire or similar data-structures in observed response patterns. These deterministic dependencies have the form of logical formulas connecting the items...
  
  
• Bootstrap aggregating
  Bootstrap aggregating
  Bootstrap aggregating is a machine learning ensemble meta-algorithm to improve machine learning of statistical classification and regression models in terms of stability and classification accuracy. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision...
  
  
• Bootstrap error-adjusted single-sample technique
  Bootstrap error-adjusted single-sample technique
  In statistics, the bootstrap error-adjusted single-sample technique is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample. It is based on estimating a probability distribution representing what can be expected from valid samples...
  
  
• Bootstrapping (statistics)
  Bootstrapping (statistics)
  In statistics, bootstrapping is a computer-based method for assigning measures of accuracy to sample estimates . This technique allows estimation of the sample distribution of almost any statistic using only very simple methods...
  
  
• Bootstrapping populations
• Borel–Cantelli lemma
• Bose–Mesner algebra
  Bose–Mesner algebra
  In mathematics, a Bose–Mesner algebra is a set of matrices, together with set of rules for combining those matrices, such that certain conditions apply...
  
  
• Box–Behnken design
• Box–Cox distribution
• Box–Cox transformation – redirects to Power transform
  Power transform
  In statistics, the power transform is from a family of functions that are applied to create a rank-preserving transformation of data using power functions. This is a useful data processing technique used to stabilize variance, make the data more normal distribution-like, improve the correlation...
  
  
• Box–Jenkins
• Box–Muller transform
• Box–Pierce test
• Box plot
  Box plot
  In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their five-number summaries: the smallest observation , lower quartile , median , upper quartile , and largest observation...
  
  
• Branching process
  Branching process
  In probability theory, a branching process is a Markov process that models a population in which each individual in generation n produces some random number of individuals in generation n + 1, according to a fixed probability distribution that does not vary from individual to...
  
  
• Bregman divergence
  Bregman divergence
  In mathematics, the Bregman divergence or Bregman distance is similar to a metric, but does not satisfy the triangle inequality nor symmetry. There are two ways in which Bregman divergences are important. Firstly, they generalize squared Euclidean distance to a class of distances that all share...
  
  
• Breusch–Godfrey test
  Breusch–Godfrey test
  In statistics, the Breusch–Godfrey test is used to assess the validity of some of the modelling assumptions inherent in applying regression-like models to observed data series...
  
  
• Breusch–Pagan statistic – redirects to Breusch–Pagan test
• Breusch–Pagan test
• Brown–Forsythe test
• Brownian bridge
  Brownian bridge
  A Brownian bridge is a continuous-time stochastic process B whose probability distribution is the conditional probability distribution of a Wiener process W given the condition that B = B = 0.The expected value of the bridge is zero, with variance t, implying that the most...
  
  
• Brownian excursion
  Brownian excursion
  In probability theory a Brownian excursion process is a stochastic processes that is closely related to a Wiener process . Realisations of Brownian excursion processes are essentially just realisations of a Weiner process seleced to satisfy certain conditions...
  
  
• Brownian motion
  Brownian motion
  Brownian motion or pedesis is the presumably random drifting of particles suspended in a fluid or the mathematical model used to describe such random movements, which is often called a particle theory.The mathematical model of Brownian motion has several real-world applications...
  
  
• Brownian tree
  Brownian tree
  A Brownian tree, whose name is derived from Robert Brown via Brownian motion, is a form of computer art that was briefly popular in the 1990s, when home computers started to have sufficient power to simulate Brownian motion...
  
  
• Bruck–Ryser–Chowla theorem
• Burke's theorem
  Burke's theorem
  In probability theory, Burke's theorem is a theorem in queueing theory by Paul J. Burke while working at Bell Telephone Laboratories that states for an M/M/1, M/M/m or M/M/∞ queue in the steady state with arrivals a Poisson process with rate parameter λ then:# The departure process is a Poisson...
  
  
• Burr distribution
• Business statistics
  Business statistics
  Business statistics is the science of good decision making in the face of uncertainty and is used in many disciplines such as financial analysis, econometrics, auditing, production and operations including services improvement, and marketing research....
  
  
• Bühlmann model
  Bühlmann model
  The Bühlmann model is a random effects model used in credibility theory in actuarial science to determine the appropriate premium for a group of insurance contracts....
  
  
• Buzen's algorithm
  Buzen's algorithm
  In queueing theory, a discipline within the mathematical theory of probability, Buzen's algorithm is an algorithm for calculating the normalization constant G in the Gordon–Newell theorem. This method was first proposed by Jeffrey P. Buzen in 1973. Once G is computed the probability distributions...
  
  
• BV4.1 (software)
  BV4.1 (software)
  The application software BV4.1 is a user-friendly tool for decomposing and seasonally adjusting monthly or quarterly economic time series by the so-called Berlin procedure. It is being developed by the Federal Statistical Office of Germany...
  
  

C

• c-chart
• Càdlàg
  Càdlàg
  In mathematics, a càdlàg , RCLL , or corlol function is a function defined on the real numbers that is everywhere right-continuous and has left limits everywhere...
  
  
• Calculating demand forecast accuracy
  Calculating Demand Forecast Accuracy
  Calculating demand forecast accuracy is the process of determining the accuracy of forecasts made regarding customer demand for a product.-Importance of forecasts:...
  
  
• Calculus of predispositions
  Calculus of predispositions
  Calculus of predispositions is a basic part of predispositioning theory and belongs to the indeterministic procedures.-Introduction:“The key component of any indeterministic procedure is the evaluation of a position...
  
  
• CalEst
  CalEst
  CalEst is a statistics package which also includes probability functions as well as tutorials to enhance the learning of Statistics and Probability...
  
   – software
• Calibrated probability assessment
  Calibrated probability assessment
  Calibrated probability assessments are subjective probabilities assigned by individuals who have been trained to assess probabilities in a way that historically represents their uncertainty. In other words, when a calibrated person says they are "80% confident" in each of 100 predictions they...
  
  
• Calibration (probability) – subjective probability, redirects to Calibrated probability assessment
  Calibrated probability assessment
  Calibrated probability assessments are subjective probabilities assigned by individuals who have been trained to assess probabilities in a way that historically represents their uncertainty. In other words, when a calibrated person says they are "80% confident" in each of 100 predictions they...
  
  
• Calibration (statistics)
  Calibration (statistics)
  There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Thus "calibration" can mean...
  
   – the statistical calibration problem
• Cancer cluster
  Cancer cluster
  Cancer cluster is a term used by epidemiologists, statisticians, and public health workers to define an occurrence of a greater-than-expected number of cancer cases within a group of people in a geographic area over a period of time....
  
  
• Candlestick chart
  Candlestick chart
  A candlestick chart is a style of bar-chart used primarily to describe price movements of a security, derivative, or currency over time.It is a combination of a line-chart and a bar-chart, in that each bar represents the range of price movement over a given time interval. It is most often used in...
  
  
• Canonical analysis
  Canonical analysis
  In statistics, canonical analysis belongs to the family of regression methods for data analysis. Regression analysis quantifies a relationship between a predictor variable and a criterion variable by the coefficient of correlation r, coefficient of determination r², and the standard regression...
  
  
• Canonical correlation
  Canonical correlation
  In statistics, canonical correlation analysis, introduced by Harold Hotelling, is a way of making sense of cross-covariance matrices. If we have two sets of variables, x_1, \dots, x_n and y_1, \dots, y_m, and there are correlations among the variables, then canonical correlation analysis will...
  
  
• Canopy clustering algorithm
  Canopy clustering algorithm
  The canopy clustering algorithm is an unsupervised pre-clustering algorithm, often used as preprocessing step for the K-means algorithm or the Hierarchical clustering algorithm....
  
  
• Cantor distribution
• Carpet plot
  Carpet plot
  A carpet plot is any of a few different specific types of diagram.- Interaction of two independent variables :Probably the more common plot referred to as a carpet plot is one that illustrates the interacting behaviour of two independent variables, which among other things facilitates interpolation...
  
  
• Cartogram
  Cartogram
  A cartogram is a map in which some thematic mapping variable – such as travel time or Gross National Product – is substituted for land area or distance. The geometry or space of the map is distorted in order to convey the information of this alternate variable...
  
  
• Case-control
  Case-control
  A case-control study is a type of study design in epidemiology. Case-control studies are used to identify factors that may contribute to a medical condition by comparing subjects who have that condition with patients who do not have the condition but are otherwise similar .Case-control studies are...
  
   – redirects to Case-control study
• Case-control study
• Catastro of Ensenada
  Catastro of Ensenada
  In 1749 a large-scale census and statistical investigation was conducted in the Crown of Castile . It included population, territorial properties, buildings, cattle, offices, all kinds of revenue and trades, and even geographical information from each place...
  
   – a census of part of Spain
• Categorical data
  Categorical data
  In statistics, categorical data is that part of an observed dataset that consists of categorical variables, or for data that has been converted into that form, for example as grouped data...
  
  
• Categorical distribution
  Categorical distribution
  In probability theory and statistics, a categorical distribution is a probability distribution that describes the result of a random event that can take on one of K possible outcomes, with the probability of each outcome separately specified...
  
  
• Categorical variable
• Cauchy distribution
  Cauchy distribution
  The Cauchy–Lorentz distribution, named after Augustin Cauchy and Hendrik Lorentz, is a continuous probability distribution. As a probability distribution, it is known as the Cauchy distribution, while among physicists, it is known as the Lorentz distribution, Lorentz function, or Breit–Wigner...
  
  
• Cauchy–Schwarz inequality
  Cauchy–Schwarz inequality
  In mathematics, the Cauchy–Schwarz inequality , is a useful inequality encountered in many different settings, such as linear algebra, analysis, probability theory, and other areas...
  
  
• Causal Markov condition
  Causal Markov condition
  The Markov condition for a Bayesian network states that any node in a Bayesian network is conditionally independent of its nondescendents, given its parents.A node is conditionally independent of the entire network, given its Markov blanket....
  
  
• Ceiling effect
  Ceiling effect
  The term ceiling effect has two distinct meanings, referring to the level at which an independent variable no longer has an effect on a dependent variable, or to the level above which variance in an independent variable is no longer measured or estimated...
  
  
• Censored regression model
  Censored regression model
  Censored regression models commonly arise in econometrics in cases where the variable ofinterest is only observable under certain conditions. A common example is labor supply. Data are frequently available on the hours worked by employees, and a labor supply model estimates the relationship between...
  
  
• Censoring (clinical trials)
  Censoring (clinical trials)
  The term censoring is used in clinical trials to refer to mathematically removing a patient from the survival curve at the end of their follow-up time. Censoring a patient will reduce the sample size for analyzing after the time of the censorship...
  
  
• Censoring (statistics)
  Censoring (statistics)
  In statistics, engineering, and medical research, censoring occurs when the value of a measurement or observation is only partially known.For example, suppose a study is conducted to measure the impact of a drug on mortality. In such a study, it may be known that an individual's age at death is at...
  
  
• Centering matrix
  Centering matrix
  In mathematics and multivariate statistics, the centering matrix is a symmetric and idempotent matrix, which when multiplied with a vector has the same effect as subtracting the mean of the components of the vector from every component.- Definition :...
  
  
• Centerpoint (geometry)
  Centerpoint (geometry)
  In statistics and computational geometry, the notion of centerpoint is a generalization of the median to data in higher-dimensional Euclidean space...
  
   — Tukey median redirects here
• Central composite design
  Central composite design
  In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order model for the response variable without needing to use a complete three-level factorial experiment....
  
  
• Central limit theorem
  Central limit theorem
  In probability theory, the central limit theorem states conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed. The central limit theorem has a number of variants. In its common...
  
  
  • Central limit theorem (illustration) — redirects to Illustration of the central limit theorem
    Illustration of the central limit theorem
    This article gives two concrete illustrations of the central limit theorem. Both involve the sum of independent and identically-distributed random variables and show how the probability distribution of the sum approaches the normal distribution as the number of terms in the sum increases.The first...
    
    
  • Central limit theorem for directional statistics
    Central limit theorem for directional statistics
    In probability theory, the central limit theorem states conditions under which the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed....
    
    
  • Lyapunov's central limit theorem
  • Martingale central limit theorem
    Martingale central limit theorem
    In probability theory, the central limit theorem says that, under certain conditions, the sum of many independent identically-distributed random variables, when scaled appropriately, converges in distribution to a standard normal distribution...
    
    
• Central moment
  Central moment
  In probability theory and statistics, central moments form one set of values by which the properties of a probability distribution can be usefully characterised...
  
  
• Central tendency
  Central tendency
  In statistics, the term central tendency relates to the way in which quantitative data is clustered around some value. A measure of central tendency is a way of specifying - central value...
  
  
• Census
  Census
  A census is the procedure of systematically acquiring and recording information about the members of a given population. It is a regularly occurring and official count of a particular population. The term is used mostly in connection with national population and housing censuses; other common...
  
  
• Cepstrum
  Cepstrum
  A cepstrum is the result of taking the Fourier transform of the logarithm of the spectrum of a signal. There is a complex cepstrum, a real cepstrum, a power cepstrum, and phase cepstrum....
  
  
• CHAID
  CHAID
  CHAID is a type of decision tree technique, based upon adjusted significance testing . The technique was developed in South Africa and was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on this topic...
  
   — CHi-squared Automatic Interaction Detector
• Chain rule for Kolmogorov complexity
  Chain rule for Kolmogorov complexity
  The chain rule for Kolmogorov complexity is an analogue of the chain rule for information entropy, which states:H = H + HThat is, the combined randomness of two sequences X and Y is the sum of the randomness of X plus whatever randomness is left in Y once we know X.This follows immediately from the...
  
  
• Challenge-dechallenge-rechallenge
  Challenge-dechallenge-rechallenge
  Challenge-dechallenge-rechallenge is a medical testing protocol in which a medicine or drug is administered, withdrawn, then re-administered, while being monitored for adverse effects at each stage...
  
  
• Change detection
  Change detection
  In statistical analysis, change detection tries to identify changes in the probability distribution of a stochastic process or time series. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times...
  
  
  • Change detection (GIS)
    Change detection (GIS)
    Change detection for GIS is a process that measures how the attributes of a particular area have changed between two or more time periods. Change detection often involves comparing aerial photographs or satellite imagery of the area taken at different times...
    
    
• Chapman–Kolmogorov equation
• Chapman–Robbins bound
  Chapman–Robbins bound
  In statistics, the Chapman–Robbins bound or Hammersley–Chapman–Robbins bound is a lower bound on the variance of estimators of a deterministic parameter. It is a generalization of the Cramér–Rao bound; compared to the Cramér–Rao bound, it is both tighter and applicable to a wider range of problems...
  
  
• Characteristic function (probability theory)
  Characteristic function (probability theory)
  In probability theory and statistics, the characteristic function of any random variable completely defines its probability distribution. Thus it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative...
  
  
• Chauvenet's criterion
  Chauvenet's criterion
  In statistical theory, the Chauvenet's criterion is a means of assessing whether one piece of experimental data — an outlier — from a set of observations, is likely to be spurious....
  
  
• Chebyshev center
  Chebyshev center
  In geometry, the Chebyshev center of a bounded set Q having non-empty interior is the center of the minimal-radius ball enclosing the entire set Q, or, alternatively, the center of largest inscribed ball of Q ....
  
  
• Chebyshev's inequality
  Chebyshev's inequality
  In probability theory, Chebyshev’s inequality guarantees that in any data sample or probability distribution,"nearly all" values are close to the mean — the precise statement being that no more than 1/k2 of the distribution’s values can be more than k standard deviations away from the mean...
  
  
• Checking if a coin is biased — redirects to Checking whether a coin is fair
• Checking whether a coin is fair
• Cheeger bound
  Cheeger bound
  In mathematics, the Cheeger bound is a bound of the second largest eigenvalue of the transition matrix of a finite-state, discrete-time, reversible stationary Markov chain. It can be seen as a special case of Cheeger inequalities in expander graphs....
  
  
• Chemometrics
  Chemometrics
  Chemometrics is the science of extracting information from chemical systems by data-driven means. It is a highly interfacial discipline, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to...
  
  
• Chernoff bound
  Chernoff bound
  In probability theory, the Chernoff bound, named after Herman Chernoff, gives exponentially decreasing bounds on tail distributions of sums of independent random variables...
  
   – a special case of Chernoff's inequality
• Chernoff face
• Chernoff's distribution
  Chernoff's distribution
  In probability theory, Chernoff's distribution, named after Herman Chernoff, is the probability distribution of the random variablewhere W is a "two-sided" Wiener process satisfying W = 0.If...
  
  
• Chernoff's inequality
• Chi distribution
• Chi-squared distribution
• Chi-squared test
• Chinese restaurant process
• Choropleth map
  Choropleth map
  A choropleth map A choropleth map A choropleth map (Greek χώρος + πληθαίν:, ("area/region" + "multiply") is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita...
  
  
• Chow test
  Chow test
  The Chow test is a statistical and econometric test of whether the coefficients in two linear regressions on different data sets are equal. The Chow test was invented by economist Gregory Chow. In econometrics, the Chow test is most commonly used in time series analysis to test for the presence of...
  
  
• Chronux
  Chronux
  Chronux is an open-source software package developed for the loading, visualization and analysis of a variety of modalities / formats of neurobiological time series data...
  
   software
• Circular distribution
• Circular error probable
  Circular error probable
  In the military science of ballistics, circular error probable is an intuitive measure of a weapon system's precision...
  
  
• Circular statistics
  Circular statistics
  Directional statistics is the subdiscipline of statistics that deals with directions , axes or rotations in Rn...
  
   – redirects to Directional statistics
• Circular uniform distribution
  Circular uniform distribution
  In probability theory and directional statistics, a circular uniform distribution is a probability distribution on the unit circle whose density is uniform for all angles.- Description :The pdf of the circular uniform distribution is:...
  
  
• Clark–Ocone theorem
• Class membership probabilities
  Class membership probabilities
  In general proplems of classification, class membership probabilities reflect the uncertainty with which a given indivual item can be assigned to any given class. Although statistical classification methods by definition generate such probabilities, applications of classification in machine...
  
  
• Classic data sets
• Classical definition of probability
  Classical definition of probability
  The classical definition of probability is identified with the works of Pierre-Simon Laplace. As stated in his Théorie analytique des probabilités,This definition is essentially a consequence of the principle of indifference...
  
  
• Classical test theory
  Classical test theory
  Classical test theory is a body of related psychometric theory that predict outcomes of psychological testing such as the difficulty of items or the ability of test-takers. Generally speaking, the aim of classical test theory is to understand and improve the reliability of psychological...
  
    - psychometrics
• Classification rule
  Classification rule
  Given a population whose members can be potentially separated into a number of different sets or classes, a classification rule is a procedure in which the elements of the population set are each assigned to one of the classes. A perfect test is such that every element in the population is assigned...
  
  
• Classifier (mathematics)
• Climate ensemble
  Climate ensemble
  In physics, a statistical ensemble is a large set of copies of a system, considered all at once; each copy of the system representing a different possible detailed realisation of the system, consistent with the system's observed macroscopic properties....
  
  
• Clinical significance
  Clinical significance
  In medicine and psychology, clinical significance refers to either of two related but slightly dissimilar concepts whereby certain findings or differences, even if measurable or statistically confirmed, either may or may not have additional significance, either by being of a magnitude that conveys...
  
  
• Clinical study design
• Clinical trial
  Clinical trial
  Clinical trials are a set of procedures in medical research and drug development that are conducted to allow safety and efficacy data to be collected for health interventions...
  
  
• Clinical utility of diagnostic tests
  Clinical utility of diagnostic tests
  The clinical utility of a diagnostic test is its capacity to rule diagnosis in and/or out and to make a decision possible to adopt or to reject a therapeutic action. It can be integrated into clinical prediction rules for specific diseases or outcomes....
  
  
• Cliodynamics
  Cliodynamics
  thumb|Clio—detail from [[The Art of Painting|The Allegory of Painting]] by [[Johannes Vermeer]]Cliodynamics is a new multidisciplinary area of research focused at mathematical modeling of historical dynamics.-Origins:The term was originally coined by Peter...
  
  
• Closed testing procedure
  Closed testing procedure
  In statistics, the closed testing procedure is a general method for performing more than one hypothesis test simultaneously.-The closed testing principle:...
  
  
• Cluster analysis
• Cluster analysis (in marketing)
  Cluster analysis (in marketing)
  Cluster analysis is a class of statistical techniques that can be applied to data that exhibit “natural” groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is a group of relatively homogeneous cases or observations. Objects in a cluster are similar to...
  
  
• Cluster randomised controlled trial
  Cluster randomised controlled trial
  A cluster randomised controlled trial is a type of randomised controlled trial in which groups of subjects are randomised...
  
  
• Cluster sampling
  Cluster sampling
  Cluster Sampling is a sampling technique used when "natural" groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups and a sample of the groups is selected. Then the required information is...
  
  
• Cluster-weighted modeling
  Cluster-weighted modeling
  In statistics, cluster-weighted modeling is an algorithm-based approach to non-linear prediction of outputs from inputs based on density estimation using a set of models that are each notionally appropriate in a sub-region of the input space...
  
  
• Clustering high-dimensional data
  Clustering high-dimensional data
  Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of measurements at once, and...
  
  
• CMA-ES
  CMA-ES
  CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy. Evolution strategies are stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems. They belong to the class of evolutionary algorithms and evolutionary computation...
  
   (Covariance Matrix Adaptation Evolution Strategy)
• Coalescent theory
  Coalescent theory
  In genetics, coalescent theory is a retrospective model of population genetics. It attempts to trace all alleles of a gene shared by all members of a population to a single ancestral copy, known as the most recent common ancestor...
  
  
• Cochran's C test
  Cochran's C test
  In statistics, Cochran's C test , named after William G. Cochran, is a one-sided upper limit variance outlier test. The C test is used to decide if a single estimate of a variance is significantly larger than a group of variances with which the single estimate is supposed to be comparable...
  
  
• Cochran's Q test
• Cochran's theorem
  Cochran's theorem
  In statistics, Cochran's theorem, devised by William G. Cochran, is a theorem used in to justify results relating to the probability distributions of statistics that are used in the analysis of variance.- Statement :...
  
  
• Cochran-Armitage test for trend
  Cochran-Armitage test for trend
  The Cochran-Armitage test for trend, named for William Cochran and Peter Armitage, is used in categorical data analysis when the aim is to assess for the presence of an association between a variable with two categories and a variable with k categories. It modifies the chi-squared test to...
  
  
• Cochran–Mantel–Haenszel statistics
  Cochran–Mantel–Haenszel statistics
  In statistics, the Cochran–Mantel–Haenszel statistics are a collection of test statistics used in the analysis of stratified categorical data.. They are named after William G Cochran, Nathan Mantel and William Haenszel. One of these test statistics is the Cochran–Mantel–Haenszel test, which allows...
  
  
• Cochrane–Orcutt estimation
• Coding (social sciences)
  Coding (social sciences)
  Coding refers to an analytical process in which data, in both quantitative form or qualitative are categorised to facilitate analysis....
  
  
• Coefficient of coherence — redirects to Coherence (statistics)
  Coherence (statistics)
  In probability theory and statistics, coherence can have two meanings.*When dealing with personal probability assessments, or supposed probabilities derived in nonstandard ways, it is a property of self-consistency across a whole set of such assessments...
  
  
• Coefficient of determination
  Coefficient of determination
  In statistics, the coefficient of determination R2 is used in the context of statistical models whose main purpose is the prediction of future outcomes on the basis of other related information. It is the proportion of variability in a data set that is accounted for by the statistical model...
  
  
• Coefficient of dispersion
  Coefficient of dispersion
  In probability theory and statistics, the index of dispersion, dispersion index, coefficient of dispersion, or variance-to-mean ratio , like the coefficient of variation, is a normalized measure of the dispersion of a probability distribution: it is a measure used to quantify whether a set of...
  
  
• Coefficient of variation
  Coefficient of variation
  In probability theory and statistics, the coefficient of variation is a normalized measure of dispersion of a probability distribution. It is also known as unitized risk or the variation coefficient. The absolute value of the CV is sometimes known as relative standard deviation , which is...
  
  
• Cognitive pretesting
  Cognitive pretesting
  Cognitive interviewing is a field research method used primarily in pre-testing survey instruments developed in collaboration by psychologists and survey researchers. It allows survey researchers to collect verbal information regarding survey responses and is used in evaluating whether the...
  
  
• Cohen's class distribution function
  Cohen's class distribution function
  Bilinear time–frequency distributions, or quadratic time–frequency distributions, arise in a sub-field field of signal analysis and signal processing called time–frequency signal processing, and, in the statistical analysis of time series data...
  
   – a time-frequency distribution function
• Cohen's kappa
  Cohen's kappa
  Cohen's kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator agreement for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation since κ takes into account the agreement occurring by chance. Some...
  
  
• Coherence (statistics)
  Coherence (statistics)
  In probability theory and statistics, coherence can have two meanings.*When dealing with personal probability assessments, or supposed probabilities derived in nonstandard ways, it is a property of self-consistency across a whole set of such assessments...
  
  
• Cohort (statistics)
  Cohort (statistics)
  In statistics and demography, a cohort is a group of subjects who have shared a particular time together during a particular time span . Cohorts may be tracked over extended periods in a cohort study. The cohort can be modified by censoring, i.e...
  
  
• Cohort effect
  Cohort effect
  The term cohort effect is used in social science to describe variations in the characteristics of an area of study over time among individuals who are defined by some shared temporal experience or common life experience, such as year of birth, or year of exposure to radiation.Cohort effects are...
  
  
• Cohort study
  Cohort study
  A cohort study or panel study is a form of longitudinal study used in medicine, social science, actuarial science, and ecology. It is an analysis of risk factors and follows a group of people who do not have the disease, and uses correlations to determine the absolute risk of subject contraction...
  
  
• Cointegration
  Cointegration
  Cointegration is a statistical property of time series variables. Two or more time series are cointegrated if they share a common stochastic drift.-Introduction:...
  
  
• Collectively exhaustive events
• Collider (epidemiology)
  Collider (epidemiology)
  In epidemiology, a collider is a variable which is the effect of two other variables. It is known as collider because, in graphical models, the other variables lead to the collider in a way that their arrow heads appear to collide on the same node that is the collider e.g.M \rightarrow P...
  
  
• Combinatorial data analysis
  Combinatorial data analysis
  Combinatorial data analysis is the study of data sets where the arrangement of objects is important. CDA can be used either to determine how well a given combinatorial construct reflects the observed data, or to search for a suitable combinatorial construct that does fit the data.-See...
  
  
• Combinatorial design
  Combinatorial design
  Combinatorial design theory is the part of combinatorial mathematics that deals with the existence and construction of systems of finite sets whose intersections have specified numerical properties....
  
  
• Combinatorial meta-analysis
• Common mode failure
• Common-cause and special-cause
  Common-cause and special-cause
  Common- and special-causes are the two distinct origins of variation in a process, as defined in the statistical thinking and methods of Walter A. Shewhart and W. Edwards Deming...
  
  
• Comparing means
  Comparing means
  The following tables provide guidance to the selection of the proper parametric or non-parametric statistical tests for a given data set.-Is there a difference ?:...
  
  
• Comparison of general and generalized linear models
• Comparison of statistical packages
  Comparison of statistical packages
  The following tables compare general and technical information for a number of statistical analysis packages.-General information:Basic information about each product...
  
  
• Comparisonwise error rate
• Complementary event
  Complementary event
  In probability theory, the complement of any event A is the event [not A], i.e. the event that A does not occur. The event A and its complement [not A] are mutually exclusive and exhaustive. Generally, there is only one event B such that A and B are both mutually exclusive and...
  
  
• Complete-linkage clustering
  Complete-linkage clustering
  In cluster analysis, complete linkage or farthest neighbour is a method of calculating distances between clusters in agglomerative hierarchical clustering...
  
  
• Complete spatial randomness
  Complete spatial randomness
  Complete spatial randomness describes a point process whereby point events occur within a given study area in a completely random fashion. Such a process is often modeled using only one parameter, i.e. the density of points, \rho within the defined area...
  
  
• Completely randomized design
  Completely randomized design
  In the design of experiments, completely randomized designs are for studying the effects of one primary factor without the need to take other nuisance variables into account. This article describes completely randomized designs that have one primary factor. The experiment compares the values of a...
  
  
• Completeness (statistics)
  Completeness (statistics)
  In statistics, completeness is a property of a statistic in relation to a model for a set of observed data. In essence, it is a condition which ensures that the parameters of the probability distribution representing the model can all be estimated on the basis of the statistic: it ensures that the...
  
  
• Compositional data
  Compositional data
  In statistics, compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information.This definition, given by John Aitchison has several consequences:...
  
  
• Composite bar chart
  Composite bar chart
  Composite bar charts are bar charts which always total 100, but each element is shown as a percentage of the bar allowing different sample sizes to be more easily compared.-External links:...
  
  
• Compound Poisson distribution
  Compound Poisson distribution
  In probability theory, a compound Poisson distribution is the probability distribution of the sum of a "Poisson-distributed number" of independent identically-distributed random variables...
  
  
• Compound Poisson process
  Compound Poisson process
  A compound Poisson process is a continuous-time stochastic process with jumps. The jumps arrive randomly according to a Poisson process and the size of the jumps is also random, with a specified probability distribution...
  
  
• Compound probability distribution
  Compound probability distribution
  In probability theory, a compound probability distribution is the probability distribution that results from assuming that a random variable is distributed according to some parametrized distribution F with an unknown parameter θ that is distributed according to some other distribution G, and then...
  
  
• Computational formula for the variance
• Computational learning theory
  Computational learning theory
  In theoretical computer science, computational learning theory is a mathematical field related to the analysis of machine learning algorithms.-Overview:Theoretical results in machine learning mainly deal with a type of...
  
  
• Computational statistics
  Computational statistics
  Computational statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science specific to the mathematical science of statistics....
  
  
• Computer experiment
  Computer experiment
  In the scientific context, a computer experiment refer to mathematical modeling using computer simulation. It has become common to call such experiments in silico...
  
  
• Concordance correlation coefficient
  Concordance correlation coefficient
  In statistics, the concordance correlation coefficient measures the agreement between two variables, e.g., to evaluate reproducibility or for inter-rater reliability.-Definition:...
  
  
• Concordant pair
• Concrete illustration of the central limit theorem
• Concurrent validity
  Concurrent validity
  Concurrent validity is a parameter used in sociology, psychology, and other psychometric or behavioral sciences. Concurrent validity is demonstrated where a test correlates well with a measure that has previously been validated. The two measures may be for the same construct, or for different, but...
  
  
• Conditional change model
  Conditional change model
  The conditional change model in statistics is the analytic procedure in which change scores are regressed on baseline values, together with the explanatory variables of interest . The method has some substantial advantages over the usual two-sample t-test recommended in textbooks.-References:*...
  
  
• Conditional distribution — redirects to Conditional probability distribution
• Conditional expectation
  Conditional expectation
  In probability theory, a conditional expectation is the expected value of a real random variable with respect to a conditional probability distribution....
  
  
• Conditional independence
  Conditional independence
  In probability theory, two events R and B are conditionally independent given a third event Y precisely if the occurrence or non-occurrence of R and the occurrence or non-occurrence of B are independent events in their conditional probability distribution given Y...
  
  
• Conditional probability
  Conditional probability
  In probability theory, the "conditional probability of A given B" is the probability of A if B is known to occur. It is commonly notated P, and sometimes P_B. P can be visualised as the probability of event A when the sample space is restricted to event B...
  
  
• Conditional probability distribution
• Conditional random field
  Conditional random field
  A conditional random field is a statistical modelling method often applied in pattern recognition.More specifically it is a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between observations and construct consistent interpretations...
  
  
• Conditional variance
  Conditional variance
  In probability theory and statistics, a conditional variance is the variance of a conditional probability distribution. Particularly in econometrics, the conditional variance is also known as the scedastic function or skedastic function...
  
  
• Conditionality principle
  Conditionality principle
  The conditionality principle is a Fisherian principle of statistical inference that Allan Birnbaum formally defined and studied in his 1962 JASA article. Together with the sufficiency principle, Birnbaum's version of the principle implies the famous likelihood principle...
  
  
• Confidence band
  Confidence band
  A confidence band is used in statistical analysis to represent the uncertainty in an estimate of a curve or function based on limited or noisy data. Confidence bands are often used as part of the graphical presentation of results in a statistical analysis...
  
  
• Confidence distribution
  Confidence distribution
  In statistics, the concept of a confidence distribution has often been loosely referred to as a distribution function on the parameter space that can represent confidence intervals of all levels for a parameter of interest...
  
  
• Confidence interval
  Confidence interval
  In statistics, a confidence interval is a particular kind of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval , in principle different from sample to sample, that frequently includes the parameter of interest, if the...
  
  
• Confidence region
  Confidence region
  In statistics, a confidence region is a multi-dimensional generalization of a confidence interval. It is a set of points in an n-dimensional space, often represented as an ellipsoid around a point which is an estimated solution to a problem, although other shapes can occur.The confidence region is...
  
  
• Configural frequency analysis
  Configural frequency analysis
  Configural frequency analysis is a method of exploratory data analysis. The goal of a configural frequency analysis is to detect patterns in the data that occur significantly more or significantly less often than expected by chance...
  
  
• Confirmation bias
  Confirmation bias
  Confirmation bias is a tendency for people to favor information that confirms their preconceptions or hypotheses regardless of whether the information is true.David Perkins, a geneticist, coined the term "myside bias" referring to a preference for "my" side of an issue...
  
  
• Confirmatory factor analysis
  Confirmatory factor analysis
  In statistics, confirmatory factor analysis is a special form of factor analysis. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct . In contrast to exploratory factor analysis, where all loadings are free to vary,...
  
  
• Confounding
  Confounding
  In statistics, a confounding variable is an extraneous variable in a statistical model that correlates with both the dependent variable and the independent variable...
  
  
• Confounding factor
• Confusion of the inverse
  Confusion of the inverse
  Confusion of the inverse, also called the conditional probability fallacy, is a logical fallacy whereupon a conditional probability is equivocated with its inverse: That is, given two events A and B, the probability Pr is assumed to be approximately equal to Pr.-Example 1:In one study, physicians...
  
  
• Conjoint analysis
  Conjoint analysis
  Conjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations...
  
  
  • Conjoint analysis (in healthcare)
    Conjoint analysis (in healthcare)
    -Why conjoint in healthcare market research?:Pharmaceutical manufacturers need deeper and deeper market information they can rely on to make the right decisions and to identify the most promising market opportunities[1][6] . They can obtain great benefits from understanding physicians’ prescription...
    
    
  • Conjoint analysis (in marketing)
    Conjoint analysis (in marketing)
    Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service....
    
    
• Conjugate prior
  Conjugate prior
  In Bayesian probability theory, if the posterior distributions p are in the same family as the prior probability distribution p, the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood...
  
  
• Consensus-based assessment
• Consensus forecast
• Consistency (statistics)
  Consistency (statistics)
  In statistics, consistency of procedures such as confidence intervals or hypothesis tests involves their behaviour as the number of items in the data-set to which they are applied increases indefinitely...
  
   (disambiguation)
• Consistent estimator
  Consistent estimator
  In statistics, a sequence of estimators for parameter θ0 is said to be consistent if this sequence converges in probability to θ0...
  
  
• Constant elasticity of substitution
  Constant Elasticity of Substitution
  In economics, Constant elasticity of substitution is a property of some production functions and utility functions.More precisely, it refers to a particular type of aggregator function which combines two or more types of consumption, or two or more types of productive inputs into an aggregate...
  
  
• Constant false alarm rate
  Constant false alarm rate
  Constant false alarm rate detection refers to a common form of adaptive algorithm used in radar systems to detect target returns against a background of noise, clutter and interference.Other detection algorithms are not adaptive...
  
  
• Constraint (information theory)
  Constraint (information theory)
  Constraint in information theory refers to the degree of statistical dependence between or among variables.Garner provides a thorough discussion of various forms of constraint with application to pattern recognition and psychology....
  
  
• Consumption distribution
  Consumption distribution
  In economics, the consumption distribution is an alternative to the income distribution for judging economic inequality, comparing levels of consumption rather than income or wealth.-See also:* Economic inequality* Wealth condensation* Lorenz curve* Asset...
  
  
• Contact process (mathematics)
• Content validity
  Content validity
  In psychometrics, content validity refers to the extent to which a measure represents all facets of a given social construct. For example, a depression scale may lack content validity if it only assesses the affective dimension of depression but fails to take into account the behavioral dimension...
  
  
• Contiguity (probability theory)
  Contiguity (probability theory)
  In probability theory, two sequences of probability measures are said to be contiguous if asymptotically they share the same support. Thus the notion of contiguity extends the concept of absolute continuity to the sequences of measures....
  
  
• Contingency table
  Contingency table
  In statistics, a contingency table is a type of table in a matrix format that displays the frequency distribution of the variables...
  
  
• Continuity correction
  Continuity correction
  In probability theory, if a random variable X has a binomial distribution with parameters n and p, i.e., X is distributed as the number of "successes" in n independent Bernoulli trials with probability p of success on each trial, then...
  
  
• Continuous distribution — redirects to Continuous probability distribution
• Continuous mapping theorem
  Continuous mapping theorem
  In probability theory, the continuous mapping theorem states that continuous functions are limit-preserving even if their arguments are sequences of random variables. A continuous function, in Heine’s definition, is such a function that maps convergent sequences into convergent sequences: if xn → x...
  
  
• Continuous probability distribution
• Continuous stochastic process
  Continuous stochastic process
  In the probability theory, a continuous stochastic process is a type of stochastic process that may be said to be "continuous" as a function of its "time" or index parameter. Continuity is a nice property for a process to have, since it implies that they are well-behaved in some sense, and,...
  
  
• Continuous-time Markov process
• Continuous-time stochastic process
  Continuous-time stochastic process
  In probability theory and statistics, a continuous-time stochastic process, or a continuous-space-time stochastic process is a stochastic process for which the index variable takes a continuous set of values, as contrasted with a discrete-time process for which the index variable takes only...
  
  
• Contrast (statistics)
  Contrast (statistics)
  In statistics, particularly analysis of variance, a contrast is a linear combination of two or more factor level means whose coefficients add up to zero. A simple contrast is the difference between two means...
  
  
• Control chart
  Control chart
  Control charts, also known as Shewhart charts or process-behaviour charts, in statistical process control are tools used to determine whether or not a manufacturing or business process is in a state of statistical control.- Overview :...
  
  
• Control event rate
  Control event rate
  In epidemiology and biostatistics, the control event rate is a measure of how often a particular statistical event occurs within the scientific control group of an experiment ....
  
  
• Control limits
  Control limits
  Control limits, also known as natural process limits, are horizontal lines drawn on a statistical process control chart, usually at a distance of ±3 standard deviations of the plotted statistic from the statistic's mean....
  
  
• Control variate
  Control variate
  The control variates method is a variance reduction technique used in Monte Carlo methods. It exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.-Underlying principle:...
  
  
• Controlling for a variable
  Controlling for a variable
  Controlling for a variable refers to the deliberate varying of the experimental conditions in order to see the impact of a specific variable when predicting the outcome variable . Controlling tends to reduce the experimental error...
  
  
• Convergence of measures
  Convergence of measures
  In mathematics, more specifically measure theory, there are various notions of the convergence of measures. Three of the most common notions of convergence are described below.-Total variation convergence of measures:...
  
  
• Convergence of random variables
  Convergence of random variables
  In probability theory, there exist several different notions of convergence of random variables. The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes...
  
  
• Convex hull
  Convex hull
  In mathematics, the convex hull or convex envelope for a set of points X in a real vector space V is the minimal convex set containing X....
  
  
• Convolution of probability distributions
  Convolution of probability distributions
  The convolution of probability distributions arises in probability theory and statistics as the operation in terms of probability distributions that corresponds to the addition of independent random variables and, by extension, to forming linear combinations of random variables...
  
  
• Convolution random number generator
• Conway–Maxwell–Poisson distribution
• Cook's distance
  Cook's distance
  In statistics, Cook's distance is a commonly used estimate of the influence of a data point when doing least squares regression analysis. In a practical ordinary least squares analysis, Cook's distance can be used in several ways: to indicate data points that are particularly worth checking for...
  
  
• Cophenetic correlation
  Cophenetic correlation
  In statistics, and especially in biostatistics, cophenetic correlation is a measure of how faithfully a dendrogram preserves the pairwise distances between the original unmodeled data points...
  
  
• Copula (statistics)
  Copula (statistics)
  In probability theory and statistics, a copula can be used to describe the dependence between random variables. Copulas derive their name from linguistics....
  
  
• Correct sampling
  Correct sampling
  During sampling of particulate materials, correct sampling is defined in Gy's sampling theory as a sampling scenario in which all particles in a population have the same probability of ending up in the sample ....
  
  
• Correction for attenuation
  Correction for attenuation
  Correction for attenuation is a statistical procedure, due to Spearman , to "rid a correlation coefficient from the weakening effect of measurement error" , a phenomenon also known as regression dilution. In measurement and statistics, it is also called disattenuation...
  
  
• Correlate summation analysis
  Correlate summation analysis
  Correlate summation analysis is a data mining method. It is designed to find the variables that are most covariant with all of the other variables being studied, relative to clustering. Aggregate correlate summation is the product of the totaled negative logarithm of the p-values for all of the...
  
  
• Correlation
  Correlation
  In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence....
  
  
• Correlation and dependence
• Correlation does not imply causation
  Correlation does not imply causation
  "Correlation does not imply causation" is a phrase used in science and statistics to emphasize that correlation between two variables does not automatically imply that one causes the other "Correlation does not imply causation" (related to "ignoring a common cause" and questionable cause) is a...
  
  
• Correlation clustering
  Correlation clustering
  In machine learning, correlation clustering or cluster editing operates in a scenario where the relationship between the objects are known instead of the actual representation of the objects...
  
  
• Correlation function
  Correlation function
  A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points...
  
  
  • Correlation function (astronomy)
    Correlation function (astronomy)
    In astronomy, a correlation function describes the distribution of galaxies in the universe. By default, correlation function refers to the two-point autocorrelation function. For a given distance, the two-point autocorrelation function is a function of one variable which describes the...
    
    
  • Correlation function (quantum field theory)
    Correlation function (quantum field theory)
    In quantum field theory, the matrix element computed by inserting a product of operators between two states, usually the vacuum states, is called a correlation function....
    
    
  • Correlation function (statistical mechanics)
    Correlation function (statistical mechanics)
    In statistical mechanics, the correlation function is a measure of the order in a system, as characterized by a mathematical correlation function, and describes how microscopic variables at different positions are correlated....
    
    
• Correlation implies causation
  Correlation implies causation
  "Correlation does not imply causation" is a phrase used in science and statistics to emphasize that correlation between two variables does not automatically imply that one causes the other "Correlation does not imply causation" (related to "ignoring a common cause" and questionable cause) is a...
  
  
• Correlation inequality
  Correlation inequality
  In probability and statistics, a correlation inequality is one of a number of inequalities satisfied by the correlation functions of a model. Such inequalities are of particular use in statistical mechanics and in percolation theory.Examples include:...
  
  
• Correlation ratio
  Correlation ratio
  In statistics, the correlation ratio is a measure of the relationship between the statistical dispersion within individual categories and the dispersion across the whole population or sample. The measure is defined as the ratio of two standard deviations representing these types of variation...
  
  
• Correlogram
  Correlogram
  In the analysis of data, a correlogram is an image of correlation statistics. For example, in time series analysis, a correlogram, also known as an autocorrelation plot, is a plot of the sample autocorrelations r_h\, versus h\, ....
  
  
• Correspondence analysis
  Correspondence analysis
  Correspondence analysis is a multivariate statistical technique proposed by Hirschfeld and later developed by Jean-Paul Benzécri. It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data...
  
  
• Cosmic variance
  Cosmic variance
  Cosmic variance is the statistical uncertainty inherent in observations of the universe at extreme distances. It is based on the idea that it is only possible to observe part of the universe at one particular time, so it is difficult to make statistical statements about cosmology on the scale of...
  
  
• Cost-of-living index
  Cost-of-living index
  Cost of living is the cost of maintaining a certain standard of living. Changes in the cost of living over time are often operationalized in a cost of living index. Cost of living calculations are also used to compare the cost of maintaining a certain standard of living in different geographic areas...
  
  
• Count data
• Counternull
  Counternull
  In statistics, and especially in the statistical analysis of psychological data, the counternull is a statistic used to aid the understanding and presentation of research results...
  
  
• Counting process
• Covariance
  Covariance
  In probability theory and statistics, covariance is a measure of how much two variables change together. Variance is a special case of the covariance when the two variables are identical.- Definition :...
  
  
• Covariance and correlation
  Covariance and correlation
  In probability theory and statistics, the mathematical descriptions of covariance and correlation are very similar. Both describe the degree of similarity between two random variables or sets of random variables....
  
  
• Covariance intersection
  Covariance intersection
  Covariance intersection is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown.-Specification:...
  
  
• Covariance matrix
  Covariance matrix
  In probability theory and statistics, a covariance matrix is a matrix whose element in the i, j position is the covariance between the i th and j th elements of a random vector...
  
  
• Covariance function
  Covariance function
  In probability theory and statistics, covariance is a measure of how much two variables change together and the covariance function describes the variance of a random variable process or field...
  
  
• Covariate
  Covariate
  In statistics, a covariate is a variable that is possibly predictive of the outcome under study. A covariate may be of direct interest or it may be a confounding or interacting variable....
  
  
• Cover's theorem
  Cover's Theorem
  Cover's Theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications...
  
  
• Coverage probability
  Coverage probability
  In statistics, the coverage probability of a confidence interval is the proportion of the time that the interval contains the true value of interest. For example, suppose our interest is in the mean number of months that people with a particular type of cancer remain in remission following...
  
  
• Cox process
  Cox process
  A Cox process , also known as a doubly stochastic Poisson process or mixed Poisson process, is a stochastic process which is a generalization of a Poisson process...
  
  
• Cox's theorem
  Cox's theorem
  Cox's theorem, named after the physicist Richard Threlkeld Cox, is a derivation of the laws of probability theory from a certain set of postulates. This derivation justifies the so-called "logical" interpretation of probability. As the laws of probability derived by Cox's theorem are applicable to...
  
  
• Cox–Ingersoll–Ross model
• Cramér–Rao bound
• Cramér–von Mises criterion
• Cramér's theorem
  Cramér's theorem
  In mathematical statistics, Cramér's theorem is one of several theorems of Harald Cramér, a Swedish statistician and probabilist.- Normal random variables :...
  
  
• Cramér's V
  Cramér's V
  In statistics, Cramér's V is a popular measure of association between two nominal variables, giving a value between 0 and +1...
  
  
• Craps principle
  Craps principle
  In probability theory, the craps principle is a theorem about event probabilities under repeated iid trials. Let E_1 and E_2 denote two mutually exclusive events which might occur on a given trial...
  
  
• Credible interval
  Credible interval
  In Bayesian statistics, a credible interval is an interval in the domain of a posterior probability distribution used for interval estimation. The generalisation to multivariate problems is the credible region...
  
  
• Cricket statistics
  Cricket statistics
  Cricket is a sport that generates a large number of statistics.Statistics are recorded for each player during a match, and aggregated over a career. At the professional level, statistics for Test cricket, one-day internationals, and first-class cricket are recorded separately...
  
  
• Crime statistics
  Crime statistics
  Crime statistics attempt to provide statistical measures of the crime in societies. Given that crime is usually secretive by nature, measurements of it are likely to be inaccurate....
  
  
• Critical region — redirects to Statistical hypothesis testing
  Statistical hypothesis testing
  A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study . In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold...
  
  
• Cromwell's rule
  Cromwell's rule
  Cromwell's rule, named by statistician Dennis Lindley, states that one should avoid using prior probabilities of 0 or 1, except when applied to statements that are logically true or false...
  
  
• Cronbach's α
  Cronbach's alpha
  Cronbach's \alpha is a coefficient of reliability. It is commonly used as a measure of the internal consistency or reliability of a psychometric test score for a sample of examinees. It was first named alpha by Lee Cronbach in 1951, as he had intended to continue with further coefficients...
  
  
• Cross-correlation
  Cross-correlation
  In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long-duration signal for a shorter, known feature...
  
  
• Cross-covariance
• Cross-entropy method
  Cross-entropy method
  The cross-entropy method attributed to Reuven Rubinstein is a general Monte Carlo approach tocombinatorial and continuous multi-extremal optimization and importance sampling.The method originated from the field of rare event simulation, where...
  
  
• Cross-sectional data
  Cross-sectional data
  Cross-sectional data or cross section in statistics and econometrics is a type of one-dimensional data set. Cross-sectional data refers to data collected by observing many subjects at the same point of time, or without regard to differences in time...
  
  
• Cross-sectional regression
  Cross-sectional regression
  A Cross-sectional regression is a type of regression model in which the explained and explanatory variables are associated with one period or point in time...
  
  
• Cross-sectional study
  Cross-sectional study
  Cross-sectional studies form a class of research methods that involve observation of all of a population, or a representative subset, at one specific point in time...
  
  
• Cross-spectrum
  Cross-spectrum
  In time series analysis, the cross-spectrum is used as part of a frequency domain analysis of the cross correlation or cross covariance between two time series.- Definition :...
  
  
• Cross tabulation
  Cross tabulation
  Cross tabulation is the process of creating a contingency table from the multivariate frequency distribution of statistical variables. Heavily used in survey research, cross tabulations can be produced by a range of statistical packages, including some that are specialised for the task. Survey...
  
  
• Cross-validation (statistics)
• Crystal Ball function
  Crystal Ball function
  The Crystal Ball function, named after the Crystal Ball Collaboration , is a probability density function commonly used to model various lossy processes in high-energy physics. It consists of a Gaussian core portion and a power-law low-end tail, below a certain threshold...
  
   - a probability distribution
• Cumulant
  Cumulant
  In probability theory and statistics, the cumulants κn of a probability distribution are a set of quantities that provide an alternative to the moments of the distribution. The moments determine the cumulants in the sense that any two probability distributions whose moments are identical will have...
  
  
• Cumulant generating function — redirects to cumulant
  Cumulant
  In probability theory and statistics, the cumulants κn of a probability distribution are a set of quantities that provide an alternative to the moments of the distribution. The moments determine the cumulants in the sense that any two probability distributions whose moments are identical will have...
  
  
• Cumulative distribution function
  Cumulative distribution function
  In probability theory and statistics, the cumulative distribution function , or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far"...
  
  
• Cumulative frequency analysis
  Cumulative frequency analysis
  Cumulative frequency analysis is the applcation of estimation theory to exceedance probability . The complement, the non-exceedance probability concerns the frequency of occurrence of values of a phenomenon staying below a reference value. The phenomenon may be time or space dependent...
  
  
• Cumulative incidence
  Cumulative incidence
  Cumulative incidence or incidence proportion is a measure of frequency, as in epidemiology, where it is a measure of disease frequency during a period of time...
  
  
• Cunningham function
  Cunningham function
  In statistics, the Cunningham function or Pearson–Cunningham function ωm,n is a generalisation of a special function introduced by and studied in the form here by...
  
  
• CURE data clustering algorithm
  CURE data clustering algorithm
  CURE is an efficient data clustering algorithm for large databases that is more robust to outliers and identifies clusters having non-spherical shapes and wide variances in size.- Drawbacks of traditional algorithms :...
  
  
• Curve fitting
  Curve fitting
  Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function...
  
  
• CUSUM
  CUSUM
  In statistical quality control, the CUSUM is a sequential analysis technique due to E. S. Page of the University of Cambridge. It is typically used for monitoring change detection...
  
  
• Cuzick–Edwards test
• Cyclostationary process

D

• d'
  D'
  The sensitivity index or d' is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, in units of the standard deviation of the noise distribution....
  
  
• d-separation
• D'Agostino's K-squared test
  D'Agostino's K-squared test
  In statistics, D’Agostino’s K2 test is a goodness-of-fit measure of departure from normality, that is the test aims to establish whether or not the given sample comes from a normally distributed population...
  
  
• Dagum distribution
• DAP
  DAP (software)
  Dap is a statistics and graphics program, that performs data management, analysis, and graphical visualization tasks which are commonly required in statistical consulting practice....
  
   — open source software
• Data analysis
  Data analysis
  Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making...
  
  
• Data assimilation
  Data assimilation
  Applications of data assimilation arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. Data assimilation proceeds by analysis cycles...
  
  
• Data binning
  Data binning
  Data binning is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall in a given small interval, a bin, are replaced by a value representative of that interval, often the central value...
  
  
• Data classification (business intelligence)
  Data classification (Business Intelligence)
  In business intelligence, data classification has close ties to data clustering, but where data clustering is descriptive, data classification is predictive. In essence data classification consists of using variables with known values to predict the unknown or future values of other variables. It...
  
  
• Data cleansing
  Data cleansing
  Data cleansing, data cleaning, or data scrubbing is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc...
  
  
• Data clustering
  Data clustering
  Cluster analysis or clustering is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters....
  
  
• Data collection
  Data collection
  Data collection is a term used to describe a process of preparing and collecting data, for example, as part of a process improvement or similar project. The purpose of data collection is to obtain information to keep on record, to make decisions about important issues, to pass information on to...
  
  
• Data Desk
  Data Desk
  Data Desk is a software program for visual data analysis, visual data exploration, and statistics. It carries out Exploratory Data Analysis and standard statistical analyses by means of dynamically linked graphic data displays that update any change simultaneously.-History:Data Desk was developed...
  
   – software
• Data dredging
  Data dredging
  Data dredging is the inappropriate use of data mining to uncover misleading relationships in data. Data-snooping bias is a form of statistical bias that arises from this misuse of statistics...
  
  
• Data generating process (disambiguation)
• Data mining
  Data mining
  Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
  
  
• Data reduction
  Data reduction
  Data Reduction is the transformation of numerical or alphabetical digital information derived empirical or experimentally into a corrected, ordered, and simplified form....
  
  
• Data point
  Data point
  In statistics, a data point is a set of measurements on a single member of a statistical population, or a subset of those measurements for a given individual...
  
  
• Data quality assurance
  Data quality assurance
  Data quality assurance is the process of profiling the data to discover inconsistencies, and other anomalies in the data and performing data cleansing activities Data quality assurance is the process of profiling the data to discover inconsistencies, and other anomalies in the data and performing...
  
  
• Data set
  Data set
  A data set is a collection of data, usually presented in tabular form. Each column represents a particular variable. Each row corresponds to a given member of the data set in question. Its values for each of the variables, such as height and weight of an object or values of random numbers. Each...
  
  
• Data-snooping bias
• Data transformation (statistics)
  Data transformation (statistics)
  In statistics, data transformation refers to the application of a deterministic mathematical function to each point in a data set — that is, each data point zi is replaced with the transformed value yi = f, where f is a function...
  
  
• Data visualization
  Data visualization
  Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"....
  
  
• DataDetective
  DataDetective
  DataDetective is a data mining platform developed by Sentient Information Systems. Since 1992, this software is being applied in organizations that have the need for retrieving patterns and relations in their typically large databases...
  
    – software
• Dataplot
  Dataplot
  Dataplot is a public-domain software system for scientific visualization and statistical analysis. It was developed at the National Institute of Standards and Technology.-External links:*...
  
    – software
• Davies–Bouldin index
  Davies–Bouldin index
  The Davies–Bouldin index in 1979 is a metric for evaluating clustering algorithms. This is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset...
  
  
• Davis distribution
• De Finetti's game
• De Finetti's theorem
  De Finetti's theorem
  In probability theory, de Finetti's theorem explains why exchangeable observations are conditionally independent given some latent variable to which an epistemic probability distribution would then be assigned...
  
  
• de Moivre's law
  De Moivre's law
  De Moivre's Law is a survival model applied in actuarial science, named for Abraham de Moivre. It is a simple law of mortality based on a linear survival function.-Definition:De Moivre's law has a singleparameter \omega called the ultimate age...
  
  
• De Moivre–Laplace theorem
  De Moivre–Laplace theorem
  In probability theory, the de Moivre–Laplace theorem is a normal approximation to the binomial distribution. It is a special case of the central limit theorem...
  
  
• Decision boundary
  Decision boundary
  In a statistical-classification problem with two classes, a decision boundary or decision surface is a hypersurface that partitions the underlying vector space into two sets, one for each class...
  
  
• Decision theory
  Decision theory
  Decision theory in economics, psychology, philosophy, mathematics, and statistics is concerned with identifying the values, uncertainties and other issues relevant in a given decision, its rationality, and the resulting optimal decision...
  
  
• Decomposition of time series
• Deep sampling
  Deep sampling
  Deep sampling is a variation of statistical sampling in which precision is sacrificed for insight. Small numbers of samples are taken, with each sample containing much information. The samples are taken approximately uniformly over the resource of interest, such as time or space...
  
  
• Degenerate distribution
• Degrees of freedom (statistics)
  Degrees of freedom (statistics)
  In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary.Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the...
  
  
• Delphi method
  Delphi method
  The Delphi method is a structured communication technique, originally developed as a systematic, interactive forecasting method which relies on a panel of experts.In the standard version, the experts answer questionnaires in two or more rounds...
  
  
• Delta method
  Delta method
  In statistics, the delta method is a method for deriving an approximate probability distribution for a function of an asymptotically normal statistical estimator from knowledge of the limiting variance of that estimator...
  
  
• Demand forecasting
  Demand forecasting
  Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data...
  
  
• Deming regression
  Deming regression
  In statistics, Deming regression, named after W. Edwards Deming, is an errors-in-variables model which tries to find the line of best fit for a two-dimensional dataset...
  
  
• Demographics
  Demographics
  Demographics are the most recent statistical characteristics of a population. These types of data are used widely in sociology , public policy, and marketing. Commonly examined demographics include gender, race, age, disabilities, mobility, home ownership, employment status, and even location...
  
  
• Demography
  Demography
  Demography is the statistical study of human population. It can be a very general science that can be applied to any kind of dynamic human population, that is, one that changes over time or space...
  
  
  • Demographic statistics
    Demographic statistics
    Among the kinds of data that national leaders need are the demographic statistics of their population. Records of births, deaths, marriages, immigration and emigration and a regular census of population provide information that is key to making sound decisions about national policy.A useful summary...
    
    
• Dendrogram
  Dendrogram
  A dendrogram is a tree diagram frequently used to illustrate the arrangement of the clusters produced by hierarchical clustering...
  
  
• Density estimation
  Density estimation
  In probability and statistics,density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function...
  
  
• Dependent and independent variables
  Dependent and independent variables
  The terms "dependent variable" and "independent variable" are used in similar but subtly different ways in mathematics and statistics as part of the standard terminology in those subjects...
  
  
• Descriptive research
  Descriptive research
  Descriptive research, also known as statistical research, describes data and characteristics about the population or phenomenon being studied. Descriptive research answers the questions who, what, where, when, "why" and how......
  
  
• Descriptive statistics
  Descriptive statistics
  Descriptive statistics quantitatively describe the main features of a collection of data. Descriptive statistics are distinguished from inferential statistics , in that descriptive statistics aim to summarize a data set, rather than use the data to learn about the population that the data are...
  
  
• Design effect
  Design effect
  In statistics, the design effect is an adjustment used in some kinds of studies, such as cluster randomised trials, to allow for the design structure. The adjustment inflates the variance of parameter estimates, and therefore their standard errors, which is necessary to allow for correlations among...
  
  
• Design matrix
  Design matrix
  In statistics, a design matrix is a matrix of explanatory variables, often denoted by X, that is used in certain statistical models, e.g., the general linear model....
  
  
• Design of experiments
  Design of experiments
  In general usage, design of experiments or experimental design is the design of any information-gathering exercises where variation is present, whether under the full control of the experimenter or not. However, in statistics, these terms are usually used for controlled experiments...
  
  
  • The Design of Experiments
    The Design of Experiments
    The Design of Experiments is a 1935 book by the British statistician R.A. Fisher, which effectively founded the field of design of experiments. The book has been highly influential.-References:...
    
     (book by Fisher)
• Detailed balance
  Detailed balance
  The principle of detailed balance is formulated for kinetic systems which are decomposed into elementary processes : At equilibrium, each elementary process should be equilibrated by its reverse process....
  
  
• Detection theory
  Detection theory
  Detection theory, or signal detection theory, is a means to quantify the ability to discern between information-bearing energy patterns and random energy patterns that distract from the information Detection theory, or signal detection theory, is a means to quantify the ability to discern between...
  
  
• Determining the number of clusters in a data set
  Determining the number of clusters in a data set
  Determining the number of clusters in a data set, a quantity often labeled k as in the k-means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem....
  
  
• Detrended correspondence analysis
  Detrended Correspondence Analysis
  Detrended correspondence analysis is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data. For example, Hill and Gauch analyse the data of a vegetation...
  
  
• Detrended fluctuation analysis
  Detrended fluctuation analysis
  In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes In stochastic processes, chaos theory and time series...
  
  
• Deviance (statistics)
• Deviance information criterion
  Deviance information criterion
  The deviance information criterion is a hierarchical modeling generalization of the AIC and BIC . It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo simulation...
  
  
• Deviation (statistics)
  Deviation (statistics)
  In mathematics and statistics, deviation is a measure of difference for interval and ratio variables between the observed value and the mean. The sign of deviation , reports the direction of that difference...
  
  
• Deviation analysis (disambiguation)
• DFFITS — a regression diagnostic
• Dickey–Fuller test
• Difference in differences
  Difference in differences
  Difference in differences is a quasi-experimental technique used in econometrics that measures the effect of a treatment at a given period in time. It is often used to measure the change induced by a particular treatment or event, though may be subject to certain biases...
  
  
• Differential entropy
  Differential entropy
  Differential entropy is a concept in information theory that extends the idea of entropy, a measure of average surprisal of a random variable, to continuous probability distributions.-Definition:...
  
  
• Diffusion process
  Diffusion process
  In probability theory, a branch of mathematics, a diffusion process is a solution to a stochastic differential equation. It is a continuous-time Markov process with continuous sample paths....
  
  
• Diffusion-limited aggregation
  Diffusion-limited aggregation
  Diffusion-limited aggregation is the process whereby particles undergoing a random walk due to Brownian motion cluster together to form aggregates of such particles. This theory, proposed by Witten and Sander in 1981, is applicable to aggregation in any system where diffusion is the primary means...
  
  
• Dimension reduction
• Dilution assay
  Dilution assay
  The term dilution assay is generally used to designate a special type of bioassay in which one or more preparations are administered to experimental units at different dose levels inducing a measurable biological response. The dose levels are prepared by dilution in a diluent that is inert in...
  
  
• Direct relationship
  Direct relationship
  In mathematics and statistics, a positive or direct relationship is a relationship between two variables in which change in one variable is associated with a change in the other variable in the same direction. For example all linear relationships with a positive slope are direct relationships...
  
  
• Directional statistics
• Dirichlet distribution
• Dirichlet process
  Dirichlet process
  In probability theory, a Dirichlet process is a stochastic process that can be thought of as a probability distribution whose domain is itself a random distribution...
  
  
• Disattenuation
• Discrepancy function
  Discrepancy function
  A discrepancy function is a mathematical function which describes how closely a structural model conforms to observed data. Larger values of the discrepancy function indicate a poor fit of the model to data. In general, the parameter estimates for a given model are chosen so as to make the...
  
  
• Discrete choice
  Discrete choice
  In economics, discrete choice problems involve choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed...
  
  
• Discrete choice analysis
• Discrete distribution
• Discrete phase-type distribution
  Discrete phase-type distribution
  The discrete phase-type distribution is a probability distribution that results from a system of one or more inter-related geometric distributions occurring in sequence, or phases. The sequence in which each of the phases occur may itself be a stochastic process...
  
  
• Discrete probability distribution
• Discrete time
  Discrete time
  Discrete time is the discontinuity of a function's time domain that results from sampling a variable at a finite interval. For example, consider a newspaper that reports the price of crude oil once every day at 6:00AM. The newspaper is described as sampling the cost at a frequency of once per 24...
  
  
• Discretization of continuous features
  Discretization of continuous features
  In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density...
  
  
• Discriminant function analysis
  Discriminant function analysis
  Discriminant function analysis is a statistical analysis to predict a categorical dependent variable by one or more continuous or binary independent variables. It is different from an ANOVA or MANOVA, which is used to predict one or multiple continuous dependent variables by one or more...
  
  
• Discriminative model
  Discriminative model
  Discriminative models are a class of models used in machine learning for modeling the dependence of an unobserved variable y on an observed variable x...
  
  
• Disorder problem
  Disorder problem
  In the study of stochastic processes in mathematics, a disorder problem has been formulated by Kolmogorov. Specifically, the problem is use ongoing observations on a stochastic process to decide whether or not to raise an alarm that the probabilistic properties of the process have changed.An...
  
  
• Distance correlation
  Distance correlation
  In statistics and in probability theory, distance correlation is a measure of statistical dependence between two random variables or two random vectors of arbitrary, not necessarily equal dimension. Its important property is that this measure of dependence is zero if and only if the random...
  
  
• Distributed lag
  Distributed lag
  In statistics and econometrics, a distributed lag model is a model for time series data in which a regression equation is used to predict current values of a dependent variable based on both the current values of an explanatory variable and the lagged values of this explanatory variable.The...
  
  
• Divergence (statistics)
  Divergence (statistics)
  In statistics and information geometry, divergence or a contrast function is a function which establishes the “distance” of one probability distribution to the other on a statistical manifold...
  
  
• Diversity index
  Diversity index
  A diversity index is a statistic which is intended to measure the local members of a set consisting of various types of objects. Diversity indices can be used in many fields of study to assess the diversity of any population in which each member belongs to a unique group, type or species...
  
  
• Divisia index
  Divisia index
  A Divisia index is a theoretical construct to create index number series for continuous-time data on prices and quantities of goods exchanged.It is designed to incorporate quantity and price changes over time from subcomponents which are measured in different units -- e.g...
  
  
• Divisia monetary aggregates index
• Dixon's Q test
• Dominating decision rule
  Dominating decision rule
  In decision theory, a decision rule is said to dominate another if the performance of the former is sometimes better, and never worse, than that of the latter....
  
  
• Donsker's theorem
  Donsker's theorem
  In probability theory, Donsker's theorem, named after M. D. Donsker, identifies a certain stochastic process as a limit of empirical processes. It is sometimes called the functional central limit theorem....
  
  
• Doob decomposition theorem
  Doob decomposition theorem
  In the theory of discrete time stochastic processes, a part of the mathematical theory of probability, the Doob decomposition theorem gives a unique decomposition of any submartingale as the sum of a martingale and an increasing predictable process. The theorem was proved by and is named for J. L....
  
  
• Doob martingale
  Doob martingale
  A Doob martingale is a mathematical construction of a stochastic process which approximates a given random variable and has the martingale property with respect to the given filtration...
  
  
• Doob's martingale convergence theorems
  Doob's martingale convergence theorems
  In mathematics — specifically, in stochastic analysis — Doob's martingale convergence theorems are a collection of results on the long-time limits of supermartingales, named after the American mathematician Joseph Leo Doob....
  
  
• Doob's martingale inequality
  Doob's martingale inequality
  In mathematics, Doob's martingale inequality is a result in the study of stochastic processes. It gives a bound on the probability that a stochastic process exceeds any given value over a given interval of time...
  
  
• Doob–Meyer decomposition theorem
• Doomsday argument
  Doomsday argument
  The Doomsday argument is a probabilistic argument that claims to predict the number of future members of the human species given only an estimate of the total number of humans born so far...
  
  
• Dot plot (bioinformatics)
  Dot plot (bioinformatics)
  A dot plot is a graphical method that allows the comparison of two biological sequences and identify regions of close similarity between them. It is a kind of recurrence plot.-Introduction:...
  
  
• Dot plot (statistics)
  Dot plot (statistics)
  A dot chart or dot plot is a statistical chart consisting of data points plotted on a simple scale, typically using filled in circles. There are two common, yet very different, versions of the dot chart. The first is described by Wilkinson as a graph that has been used in hand-drawn graphs to...
  
  
• Double counting (fallacy)
  Double counting (fallacy)
  Double counting is a fallacy in which, when counting events or occurrences in probability or in other areas, a solution counts events two or more times, resulting in an erroneous number of events or occurrences which is higher than the true result...
  
  
• Double exponential distribution — disambiguation
• Double mass analysis
  Double mass analysis
  Double mass analysis is a commonly used data analysis approach for investigating the behaviour of records made of hydrological or meteorological data at a number of locations. It is used to determine whether there is a need for corrections to the data to account for changes in data collection...
  
  
• Doubly stochastic model
  Doubly stochastic model
  In statistics, a doubly stochastic model is a type of model that can arise in many contexts, but in particular in modelling time-series and stochastic processes....
  
  
• Drift rate — redirects to Stochastic drift
  Stochastic drift
  In probability theory, stochastic drift is the change of the average value of a stochastic process. A related term is the drift rate which is the rate at which the average changes. This is in contrast to the random fluctuations about this average value...
  
  
• Dudley's theorem
  Dudley's theorem
  In probability theory, Dudley’s theorem is a result relating the expected upper bound and regularity properties of a Gaussian process to its entropy and covariance structure. The result was proved in a landmark 1967 paper of Richard M...
  
  
• Dummy variable (statistics)
• Duncan's new multiple range test
  Duncan's new multiple range test
  In statistics, Duncan's new multiple range test is a multiple comparison procedure developed by David B. Duncan in 1955. Duncan's MRT belongs to the general class of multiple comparison procedures that use the studentized range statistic qr to compare sets of means.Duncan's new multiple range test...
  
  
• Durbin test
  Durbin test
  In the analysis of designed experiments, the Friedman test is the most common non-parametric test for complete block designs. The Durbin test is a nonparametric test for balanced incomplete designs that reduces to the Friedman test in the case of a complete block design.-Background:In a randomized...
  
  
• Durbin–Watson statistic
• Dutch book
  Dutch book
  In gambling a Dutch book or lock is a set of odds and bets which guarantees a profit, regardless of the outcome of the gamble. It is associated with probabilities implied by the odds not being coherent....
  
  
• Dvoretzky–Kiefer–Wolfowitz inequality
  Dvoretzky–Kiefer–Wolfowitz inequality
  In the theory of probability and statistics, the Dvoretzky–Kiefer–Wolfowitz inequality predicts how close an empirically determined distribution function will be to the distribution function from which the empirical samples are drawn...
  
  
• Dyadic distribution
  Dyadic distribution
  A dyadic distribution is a specific type of discrete or categorical probability distribution that is of some theoretical importance in data compression.-Definition:...
  
  
• Dynamic Bayesian network
  Dynamic Bayesian network
  A dynamic Bayesian network is a Bayesian network that represents sequences of variables. These sequences are often time-series or sequences of symbols . The hidden Markov model can be considered as a simple dynamic Bayesian network.- References :* , Zoubin Ghahramani, Lecture Notes In Computer...
  
  
• Dynamic factor

E

• E-statistic
• Earth mover's distance
  Earth Mover's Distance
  In computer science, the earth mover's distance is a measure of the distance between two probability distributions over a region D. In mathematics, this is known as the Wasserstein metric...
  
  
• Ecological correlation
  Ecological correlation
  In statistics, an ecological correlation is a correlation between two variables that are group means, in contrast to a correlation between two variables that describe individuals. For example, one might study the correlation between physical activity and weight among sixth-grade children...
  
  
• Ecological fallacy
  Ecological fallacy
  An ecological fallacy is a logical fallacy in the interpretation of statistical data in an ecological study, whereby inferences about the nature of specific individuals are based solely upon aggregate statistics collected for the group to which those individuals belong...
  
  
• Ecological study
  Ecological study
  An ecological study is an epidemiological study in which the unit of analysis is a population rather than an individual. For instance, an ecological study may look at the association between smoking and lung cancer deaths in different countries...
  
  
• Econometrics
  Econometrics
  Econometrics has been defined as "the application of mathematics and statistical methods to economic data" and described as the branch of economics "that aims to give empirical content to economic relations." More precisely, it is "the quantitative analysis of actual economic phenomena based on...
  
  
• Econometric model
  Econometric model
  Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon under study...
  
  
• Econometric software – a list of software articles
• Economic data
  Economic data
  Economic data or economic statistics may refer to data describing an actual economy, past or present. These are typically found in time-series form, that is, covering more than one time period or in cross-sectional data in one time period Economic data or economic statistics may refer to data...
  
  
• Economic epidemiology
  Economic epidemiology
  Economic epidemiology is a field at the intersection of epidemiology and economics. Its premise is to incorporate incentives for healthy behavior and their attendant behavioral responses into an epidemiological context to better understand how diseases are transmitted...
  
  
• Economic statistics
  Economic statistics
  Economic statistics is a topic in applied statistics that concerns the collection, processing, compilation, dissemination, and analysis of economic data. It is also common to call the data themselves 'economic statistics', but for this usage see economic data. The data of concern to economic ...
  
  
• Eddy covariance
  Eddy covariance
  The eddy covariance technique is a key atmospheric flux measurement technique to measure and calculate vertical turbulent fluxes within atmospheric boundary layers...
  
  
• Edgeworth series
  Edgeworth series
  The Gram–Charlier A series , and the Edgeworth series are series that approximate a probability distribution in terms of its cumulants...
  
  
• Effect size
  Effect size
  In statistics, an effect size is a measure of the strength of the relationship between two variables in a statistical population, or a sample-based estimate of that quantity...
  
  
• Efficiency (statistics)
  Efficiency (statistics)
  In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some “best possible” manner. The notion of “best possible” relies upon the choice of a particular loss function — the function which quantifies the relative degree of undesirability of estimation errors...
  
  
• Efficient estimator
• Ehrenfest model
  Ehrenfest model
  The Ehrenfest model of diffusion was proposed by Paul Ehrenfest to explain the second law of thermodynamics. The model considers N particles in two containers. Particles independently change container at a rate λ...
  
  
• Eigenpoll
  Eigenpoll
  An eigenpoll is a type of statistical survey which gathers knowledge from the community. It differs from opinion polls by finding the best solution, rather than finding the most popular opinion.- Methodology :...
  
  
• Elastic map
  Elastic map
  Elastic maps provide a tool for nonlinear dimensionality reduction. By their construction, they are system of elastic springs embedded in the dataspace. This system approximates a low-dimensional manifold...
  
  
• Elliptical distribution
  Elliptical distribution
  In probability and statistics, an elliptical distribution is any member of a broad family of probability distributions that generalize the multivariate normal distribution and inherit some of its properties.-Definition:...
  
  
• Ellsberg paradox
  Ellsberg paradox
  The Ellsberg paradox is a paradox in decision theory and experimental economics in which people's choices violate the expected utility hypothesis.An alternate viewpoint is that expected utility theory does not properly describe actual human choices...
  
  
• Elston–Stewart algorithm
• Empirical
  Empirical
  The word empirical denotes information gained by means of observation or experimentation. Empirical data are data produced by an experiment or observation....
  
  
• Empirical Bayes method
  Empirical Bayes method
  Empirical Bayes methods are procedures for statistical inference in which the prior distribution is estimated from the data. This approach stands in contrast to standardBayesian methods, for which the prior distribution is fixed before any data are observed...
  
  
• Empirical distribution function
  Empirical distribution function
  In statistics, the empirical distribution function, or empirical cdf, is the cumulative distribution function associated with the empirical measure of the sample. This cdf is a step function that jumps up by 1/n at each of the n data points. The empirical distribution function estimates the true...
  
  
• Empirical measure
  Empirical measure
  In probability theory, an empirical measure is a random measure arising from a particular realization of a sequence of random variables. The precise definition is found below. Empirical measures are relevant to mathematical statistics....
  
  
• Empirical orthogonal functions
  Empirical orthogonal functions
  In statistics and signal processing, the method of empirical orthogonal function analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the...
  
  
• Empirical probability
  Empirical probability
  Empirical probability, also known as relative frequency, or experimental probability, is the ratio of the number of "favorable" outcomes to the total number of trials, not in a sample space but in an actual sequence of experiments...
  
  
• Empirical process
  Empirical process
  The study of empirical processes is a branch of mathematical statistics and a sub-area of probability theory. It is a generalization of the central limit theorem for empirical measures...
  
  
• Empirical statistical laws
  Empirical statistical laws
  An empirical statistical law or a law of statistics represents a type of behaviour that has been found across a number of datasets and, indeed, across a range of types of data sets. Many of these observances have been formulated and proved as statistical or probabilistic theorems and the term...
  
  
• Endogeneity (economics)
  Endogeneity (economics)
  In an econometric model, a parameter or variable is said to be endogenous when there is a correlation between the parameter or variable and the error term. Endogeneity can arise as a result of measurement error, autoregression with autocorrelated errors, simultaneity, omitted variables, and sample...
  
  
• End point of clinical trials
  End point of clinical trials
  An endpoint is something which is measured in a clinical trial or study. Measuring the selected endpoints is the goal of a trial. The response rate and survival are examples of the endpoints....
  
  
• Energy distance
  Energy distance
  Energy distance is a statistical distance between probability distributions. If X and Y are independent random vectors in Rd, with cumulative distribution functions F and G respectively, then the energy distance between the distributions F and G is definedwhere X, X' are independent and identically...
  
  
• Energy statistics
  Energy statistics
  Energy statistics refers to collecting, compiling, analyzing and disseminating data on commodities such as coal, crude oil, natural gas, electricity, or renewable energy sources , when they are used for the energy they contain...
  
  
• Encyclopedia of Statistical Sciences
  Encyclopedia of Statistical Sciences
  The Encyclopedia of Statistical Sciences is the largest-ever encyclopaedia of statistics. It is published by John Wiley & Sons.The first edition, in nine volumes, was edited by Norman Lloyd Johnson and Samuel Kotz and appeared in 1982. The second edition, in 16 volumes, was published in 2006. ...
  
   (book)
• Engineering statistics
  Engineering statistics
  Engineering statistics combines engineering and statistics:# Design of Experiments is a methodology for formulating scientific and engineering problems using statistical models. The protocol specifies a randomization procedure for the experiment and specifies the primary data-analysis,...
  
  
• Engineering tolerance
• Engset calculation
• Ensemble forecasting
  Ensemble forecasting
  Ensemble forecasting is a numerical prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system...
  
  
• Ensemble Kalman filter
  Ensemble Kalman filter
  The ensemble Kalman filter is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models...
  
  
• Entropy (information theory)
• Entropy estimation
  Entropy estimation
  Estimating the differential entropy of a system or process, given some observations, is useful in various science/engineering applications, such as Independent Component Analysis, image analysis, genetic analysis, speech recognition, manifold learning, and time delay estimation...
  
  
• Entropy power inequality
  Entropy power inequality
  In mathematics, the entropy power inequality is a result in probability theory that relates to so-called "entropy power" of random variables. It shows that the entropy power of suitably well-behaved random variables is a superadditive function. The entropy power inequality was proved in 1948 by...
  
  
• Environmental statistics
  Environmental statistics
  Environmental statistics is the application of statistical methods to environmental science. It covers procedures for dealing with questions concerning both the natural environment in its undistrurbed state and the interaction of humanity with the environment...
  
  
• Epi Info
  Epi Info
  Epi Info is public domain statistical software for epidemiology developed by Centers for Disease Control and Prevention in Atlanta, Georgia ....
  
   — software
• Epidata
  Epidata
  EpiData refers to a group of applications used in combination for creating documented data structures and analysis of quantitative data. The EpiData Association, which created the software, was created in 1999 and is based in Denmark...
  
   — software
• Epidemic model
  Epidemic model
  An Epidemic model is a simplified means of describing the transmission of communicable disease through individuals.-Introduction:The outbreak and spread of disease has been questioned and studied for many years...
  
  
• Epidemiological methods
  Epidemiological methods
  The science of epidemiology has matured significantly from the times of Hippocrates and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities....
  
  
• Epilogism
  Epilogism
  Epilogism is a style of Inference invented by the ancient Empiric school of medicine. It is a theory-free method of looking at history by accumulating fact with minimal generalization and being conscious of the side effects of making causal claims .Epilogism is an inference which moves entirely...
  
  
• Epitome (image processing)
  Epitome (image processing)
  In image processing, an epitome is a condensed digital representation of the essential statistical properties of ordered datasets, such as matrices representing images, audio signals, videos, or genetic sequences...
  
  
• Epps effect
  Epps effect
  In econometrics and time series analysis, the Epps effect, named after T. W. Epps, is the phenomenon that the empirical correlation between the returns of two different stocks decreases as the sampling frequency of data increases. The phenomenon is caused by non-synchronous/asynchronous...
  
  
• Equating
  Equating
  Test equating traditionally refers to the statistical process of determining comparable scores on different forms of an exam. It can be accomplished using either classical test theory or item response theory....
  
   – test equating
• Equipossible
  Equipossible
  Equipossibility is a philosophical concept in possibility theory that is a precursor to the notion of equiprobability in probability theory. It is used to distinguish what can occur in a probability experiment...
  
  
• Equiprobable
  Equiprobable
  Equiprobability is a philosophical concept in probability theory that allows one to assign equal probabilities to outcomes when they are judged to be equipossible or to be "equally likely" in some sense...
  
  
• Erdős–Rényi model
  Erdos–Rényi model
  In graph theory, the Erdős–Rényi model, named for Paul Erdős and Alfréd Rényi, is either of two models for generating random graphs, including one that sets an edge between each pair of nodes with equal probability, independently of the other edges...
  
  
• Erlang distribution
• Ergodic theory
  Ergodic theory
  Ergodic theory is a branch of mathematics that studies dynamical systems with an invariant measure and related problems. Its initial development was motivated by problems of statistical physics....
  
  
• Ergodicity
  Ergodicity
  In mathematics, the term ergodic is used to describe a dynamical system which, broadly speaking, has the same behavior averaged over time as averaged over space. In physics the term is used to imply that a system satisfies the ergodic hypothesis of thermodynamics.-Etymology:The word ergodic is...
  
  
• Error bar
  Error bar
  Error bars are a graphical representation of the variability of data and are used on graphs to indicate the error, or uncertainty in a reported measurement. They give a general idea of how accurate a measurement is, or conversely, how far from the reported value the true value might be...
  
  
• Error correction model
  Error correction model
  An error correction model is a dynamical system with the characteristics that the deviation of the current state from its long-run relationship will be fed into its short-run dynamics....
  
  
• Error function
  Error function
  In mathematics, the error function is a special function of sigmoid shape which occurs in probability, statistics and partial differential equations...
  
  
• Errors and residuals in statistics
  Errors and residuals in statistics
  In statistics and optimization, statistical errors and residuals are two closely related and easily confused measures of the deviation of a sample from its "theoretical value"...
  
  
• Errors-in-variables models
  Errors-in-variables models
  In statistics and econometrics, errors-in-variables models or measurement errors models are regression models that account for measurement errors in the independent variables...
  
  
• An Essay towards solving a Problem in the Doctrine of Chances
  An Essay towards solving a Problem in the Doctrine of Chances
  An Essay towards solving a Problem in the Doctrine of Chances is a work on the mathematical theory of probability by the Reverend Thomas Bayes, published in 1763, two years after its author's death. It included a statement of a special case of what is now called Bayes' theorem. In 18th-century...
  
  
• Estimating equations
  Estimating equations
  In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated. This can be thought of as a generalisation of many classical methods --- the method of moments, least squares, and maximum likelihood --- as well as some recent...
  
  
• Estimation
  Estimation
  Estimation is the calculated approximation of a result which is usable even if input data may be incomplete or uncertain.In statistics,*estimation theory and estimator, for topics involving inferences about probability distributions...
  
  
• Estimation theory
  Estimation theory
  Estimation theory is a branch of statistics and signal processing that deals with estimating the values of parameters based on measured/empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the...
  
  
• Estimation of covariance matrices
  Estimation of covariance matrices
  In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. Estimation of covariance matrices then deals with the question of how to approximate the actual covariance matrix on the basis of a sample from the multivariate distribution...
  
  
• Estimation of signal parameters via rotational invariance techniques
• Estimator
  Estimator
  In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule and its result are distinguished....
  
  
• Etemadi's inequality
  Etemadi's inequality
  In probability theory, Etemadi's inequality is a so-called "maximal inequality", an inequality that gives a bound on the probability that the partial sums of a finite collection of independent random variables exceed some specified bound...
  
  
• Ethical problems using children in clinical trials
  Ethical problems using children in clinical trials
  In health care, a clinical trial is a comparison test of a medication or other medical treatment , versus a placebo , other medications or devices, or the standard medical treatment for a patient's condition....
  
  
• Event (probability theory)
  Event (probability theory)
  In probability theory, an event is a set of outcomes to which a probability is assigned. Typically, when the sample space is finite, any subset of the sample space is an event...
  
  
• Event study
  Event study
  An Event study is a statistical method to assess the impact of an event on the value of a firm. For example, the announcement of a merger between two business entities can be analyzed to see whether investors believe the merger will create or destroy value...
  
  
• Evidence under Bayes theorem
  Evidence under Bayes theorem
  Bayes' theorem provides a way of updating the probability of an event in the light of new information. In the evidence law context, for example, it could be used as a way of updating the probability that a genetic sample found at the scene of the crime came from the defendant in light of a genetic...
  
  
• Evolutionary data mining
  Evolutionary data mining
  Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps...
  
  
• Ewens's sampling formula
  Ewens's sampling formula
  In population genetics, Ewens' sampling formula, describes the probabilities associated with counts of how many different alleles are observed a given number of times in the sample.-Definition:...
  
  
• EWMA chart
  EWMA chart
  In statistical quality control, the EWMA chart is a type of control chart used to monitor either variables or attributes-type data using the monitored business or industrial process's entire history of output...
  
  
• Exact statistics
  Exact statistics
  Exact statistics, such as that described in exact test, is a branch of statistics that was developed to provide more accurate results pertaining to statistical testing and interval estimation by eliminating procedures based on asymptotic and approximate statistical methods...
  
  
• Exact test
  Exact test
  In statistics, an exact test is a test where all assumptions upon which the derivation of the distribution of the test statistic is based are met, as opposed to an approximate test, in which the approximation may be made as close as desired by making the sample size big enough...
  
  
• Examples of Markov chains
  Examples of Markov chains
  - Board games played with dice :A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. To see the...
  
  
• Excess risk
  Excess risk
  In statistics, excess risk is a measure of the association between a specified risk factor and a specified outcome...
  
  
• Exchange paradox
• Exchangeable random variables
• Expander walk sampling
  Expander walk sampling
  In the mathematical discipline of graph theory, the expander walk sampling theorem states that sampling vertices in an expander graph by doing a random walk is almost as good as sampling the vertices independently from a uniform distribution....
  
  
• Expectation-maximization algorithm
  Expectation-maximization algorithm
  In statistics, an expectation–maximization algorithm is an iterative method for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models, where the model depends on unobserved latent variables...
  
  
• Expectation propagation
  Expectation propagation
  Expectation propagation is a technique in Bayesian machine learning, developed by Thomas Minka.EP finds approximations to a probability distribution. It uses an iterative approach that leverages the factorization structure of the target distribution. It differs from other Bayesian approximation...
  
  
• Expected utility hypothesis
  Expected utility hypothesis
  In economics, game theory, and decision theory the expected utility hypothesis is a theory of utility in which "betting preferences" of people with regard to uncertain outcomes are represented by a function of the payouts , the probabilities of occurrence, risk aversion, and the different utility...
  
  
• Expected value
  Expected value
  In probability theory, the expected value of a random variable is the weighted average of all possible values that this random variable can take on...
  
  
• Expected value of sample information
  Expected value of sample information
  In decision theory, the expected value of sample information is the expected increase in utility that you could obtain from gaining access to a sample of additional observations before making a decision. The additional information obtained from the sample may allow you to make a more informed,...
  
  
• Experiment
  Experiment
  An experiment is a methodical procedure carried out with the goal of verifying, falsifying, or establishing the validity of a hypothesis. Experiments vary greatly in their goal and scale, but always rely on repeatable procedure and logical analysis of the results...
  
  
• Experimental design diagram
  Experimental Design Diagram
  Experimental Design Diagram is a diagram used by scientists, to design an experiment. This diagram helps to identify the essential components of an experiment...
  
  
• Experimental event rate
  Experimental event rate
  In epidemiology and biostatistics, the experimental event rate is a measure of how often a particular statistical event occurs within the experimental group of an experiment ....
  
  
• Experimental research design
• Experimental uncertainty analysis
  Experimental uncertainty analysis
  The purpose of this introductory article is to discuss the experimental uncertainty analysis of a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship to calculate that derived quantity...
  
  
• Experimental techniques — redirects to Experimental research design
• Experimenter's bias
  Experimenter's bias
  In experimental science, experimenter's bias is subjective bias towards a result expected by the human experimenter. David Sackett, in a useful review of biases in clinical studies, states that biases can occur in any one of seven stages of research:...
  
  
• Experimentwise error rate
  Experimentwise error rate
  In statistics, during multiple comparisons testing, experimentwise error rate is the probability of at least one false rejection of the null hypothesis over an entire experiment. The α that is assigned applies to all of the hypothesis tests as a whole, not individually as in the comparisonwise...
  
  
• Explained sum of squares
  Explained sum of squares
  In statistics, the explained sum of squares is a quantity used in describing how well a model, often a regression model, represents the data being modelled...
  
  
• Explained variation
  Explained variation
  In statistics, explained variation or explained randomness measures the proportion to which a mathematical model accounts for the variation of a given data set...
  
  
• Explanatory variable
• Exploratory data analysis
  Exploratory data analysis
  In statistics, exploratory data analysis is an approach to analysing data sets to summarize their main characteristics in easy-to-understand form, often with visual graphs, without using a statistical model or having formulated a hypothesis...
  
  
• Exponential dispersion model
  Exponential dispersion model
  Exponential dispersion models are statistical models in which the probability distribution is of a special form. This class of models represents a generalisation of the exponential family of models which themselves play an important role in statistical theory because they have a special structure...
  
  
• Exponential distribution
  Exponential distribution
  In probability theory and statistics, the exponential distribution is a family of continuous probability distributions. It describes the time between events in a Poisson process, i.e...
  
  
• Exponential family
  Exponential family
  In probability and statistics, an exponential family is an important class of probability distributions sharing a certain form, specified below. This special form is chosen for mathematical convenience, on account of some useful algebraic properties, as well as for generality, as exponential...
  
  
• Exponential-logarithmic distribution
• Exponential power distribution — redirects to Generalized normal distribution
• Exponential random numbers — redirect to subsection of Exponential distribution
  Exponential distribution
  In probability theory and statistics, the exponential distribution is a family of continuous probability distributions. It describes the time between events in a Poisson process, i.e...
  
  
• Exponential smoothing
  Exponential smoothing
  Exponential smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random process, or it may be an...
  
  
• Exponentiated Weibull distribution
  Exponentiated weibull distribution
  In statistics, the exponentiated Weibull family of probability distributions was introduced by Mudholkar and Srivastava as an extension of the Weibull family obtained by adding a second shape parameter....
  
  
• Exposure variable
• Extended Kalman filter
  Extended Kalman filter
  In estimation theory, the extended Kalman filter is the nonlinear version of the Kalman filter which linearizes about the current mean and covariance...
  
  
• Extended negative binomial distribution
  Extended negative binomial distribution
  In probability and statistics the extended negative binomial distribution is a discrete probability distribution extending the negative binomial distribution. It is a truncated version of the negative binomial distribution for which estimation methods have been studied.In the context of actuarial...
  
  
• Extensions of Fisher's method
  Extensions of Fisher's method
  In statistics, extensions of Fisher's method are a group of approaches that allow approximately valid statistical inferences to be made when the assumptions required for the direct application of Fisher's method are not valid...
  
  
• External validity
  External validity
  External validity is the validity of generalized inferences in scientific studies, usually based on experiments as experimental validity....
  
  
• Extrapolation domain analysis
  Extrapolation domain analysis
  Extrapolation domain analysis is a methodology for identifying geographical areas that seem suitable for adoption of innovative ecosystem management practices on the basis of sites exhibiting similarity in conditions such as climatic, land use and socio-economic indicators...
  
  
• Extreme value theory
  Extreme value theory
  Extreme value theory is a branch of statistics dealing with the extreme deviations from the median of probability distributions. The general theory sets out to assess the type of probability distributions generated by processes...
  
  
• Extremum estimator
  Extremum estimator
  In statistics and econometrics, extremum estimators is a wide class of estimators for parametric models that are calculated through maximization of a certain objective function, which depends on the data...
  
  

F

• F-distribution
• F-divergence
  F-divergence
  In probability theory, an ƒ-divergence is a function Df that measures the difference between two probability distributions P and Q...
  
  
• F-statistics
  F-statistics
  In population genetics, F-statistics describe the level of heterozygosity in a population; more specifically the degree of a reduction in heterozygosity when compared to Hardy–Weinberg expectation...
  
   – population genetics
• F-test
  F-test
  An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis.It is most often used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. ...
  
  
• F-test of equality of variances
  F-test of equality of variances
  In statistics, an F-test for the null hypothesis that two normal populations have the same variance is sometimes used, although it needs to be used with caution as it can be sensitive to the assumption that the variables have this distribution....
  
  
• F1 score
  F1 Score
  In statistics, the F1 score is a measure of a test's accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct results divided by the number of all returned results and r is the number of correct results divided by the number of...
  
  
• Factor analysis
  Factor analysis
  Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, uncorrelated variables called factors. In other words, it is possible, for example, that variations in three or four observed variables...
  
  
• Factor regression model
• Factor graph
  Factor graph
  In probability theory and its applications, a factor graph is a particular type of graphical model, with applications in Bayesian inference, that enables efficient computation of marginal distributions through the sum-product algorithm...
  
  
• Factorial code
  Factorial code
  Most real world data sets consist of data vectors whose individual components are not statistically independent, that is, they are redundant in the statistical sense. Then it is desirable to create a factorial code of the data, i...
  
  
• Factorial experiment
  Factorial experiment
  In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be...
  
  
• Factorial moment
• Factorial moment generating function
• Failure rate
  Failure rate
  Failure rate is the frequency with which an engineered system or component fails, expressed for example in failures per hour. It is often denoted by the Greek letter λ and is important in reliability engineering....
  
  
• Fair coin
  Fair coin
  In probability theory and statistics, a sequence of independent Bernoulli trials with probability 1/2 of success on each trial is metaphorically called a fair coin. One for which the probability is not 1/2 is called a biased or unfair coin...
  
  
• Falconer's formula
  Falconer's formula
  Falconer's formula is used in twin studies to determine the genetic heritability of a trait based on the difference between twin correlations.The formula is hb2 = 2, where hb2 is the broad sense heritability, rmz is the identical twin correlation, and rdz is the fraternal twin correlation...
  
  
• False discovery rate
  False discovery rate
  False discovery rate control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of rejected hypotheses, FDR controls the expected proportion of incorrectly rejected null hypotheses...
  
  
• False negative
  Type I and type II errors
  In statistical test theory the notion of statistical error is an integral part of hypothesis testing. The test requires an unambiguous statement of a null hypothesis, which usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or...
  
  
• False positive
  Type I and type II errors
  In statistical test theory the notion of statistical error is an integral part of hypothesis testing. The test requires an unambiguous statement of a null hypothesis, which usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or...
  
  
• False positive rate
  False positive rate
  When performing multiple comparisons in a statistical analysis, the false positive rate is the probability of falsely rejecting the null hypothesis for a particular test among all the tests performed...
  
  
• False positive paradox
  False positive paradox
  The false positive paradox is a statistical result where false positive tests are more probable than true positive tests, occurring when the overall population has a low incidence of a condition and the incidence rate is lower than the false positive rate...
  
  
• Familywise error rate
  Familywise error rate
  In statistics, familywise error rate is the probability of making one or more false discoveries, or type I errors among all the hypotheses when performing multiple pairwise tests.-Classification of m hypothesis tests:...
  
  
• Fan chart (time series)
  Fan chart (time series)
  In time series analysis, a fan chart is a chart that joins a simple line chart for observed past data, by showing ranges for possible values of future data together with a line showing a central estimate or most likely value for the future outcomes...
  
  
• Fano factor
• Fast Fourier transform
  Fast Fourier transform
  A fast Fourier transform is an efficient algorithm to compute the discrete Fourier transform and its inverse. "The FFT has been called the most important numerical algorithm of our lifetime ." There are many distinct FFT algorithms involving a wide range of mathematics, from simple...
  
  
• Fast Kalman filter
  Fast Kalman filter
  The fast Kalman filter , devised by Antti Lange , is an extension of the Helmert-Wolf blocking method from geodesy to real-time applications of Kalman filtering such as satellite imaging of the Earth...
  
  
• FastICA
  FastICA
  FastICA is an efficient and popular algorithm for independent component analysis invented by Aapo Hyvärinen at Helsinki University of Technology. The algorithm is based on a fixed-point iteration scheme maximizing non-Gaussianity as a measure of statistical independence...
  
   – fast independent component analysis
• Fat tail
  Fat tail
  A fat-tailed distribution is a probability distribution that has the property, along with the heavy-tailed distributions, that they exhibit extremely large skewness or kurtosis. This comparison is often made relative to the ubiquitous normal distribution, which itself is an example of an...
  
  
• Feasible generalized least squares
• Feature extraction
  Feature extraction
  In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction.When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation...
  
  
• Feller process
  Feller process
  In probability theory relating to stochastic processes, a Feller process is a particular kind of Markov process.-Definitions:Let X be a locally compact topological space with a countable base...
  
  
• Feller's coin-tossing constants
  Feller's coin-tossing constants
  Feller's coin-tossing constants are a set of numerical constants which describe asymptotic probabilities that in n independent tosses of a fair coin, no run of k consecutive heads appears....
  
  
• Feller-continuous process
  Feller-continuous process
  In mathematics, a Feller-continuous process is a continuous-time stochastic process for which the expected value of suitable statistics of the process at a given time in the future depend continuously on the initial condition of the process...
  
  
• Felsenstein's tree peeling algorithm
  Felsenstein's Tree Peeling Algorithm
  In statistical genetics, Felsenstein's tree-pruning algorithm , due to Joseph Felsenstein, is an algorithm for computing the likelihood of an evolutionary tree from nucleic acid sequence data....
  
   — statistical genetics
• Fides (reliability)
  Fides (reliability)
  Fides is a guide allowing estimated reliability calculation for electronic components and systems. The reliability prediction is generally expressed in FIT or MTBF...
  
  
• Fiducial inference
  Fiducial inference
  Fiducial inference is one of a number of different types of statistical inference. These are rules, intended for general application, by which conclusions can be drawn from samples of data. In modern statistical practice, attempts to work with fiducial inference have fallen out of fashion in...
  
  
• Field experiment
  Field experiment
  A field experiment applies the scientific method to experimentally examine an intervention in the real world rather than in the laboratory...
  
  
• Fieller's theorem
  Fieller's theorem
  In statistics, Fieller's theorem allows the calculation of a confidence interval for the ratio of two means.-Approximate confidence interval:...
  
  
• File drawer problem
• Filtering problem (stochastic processes)
  Filtering problem (stochastic processes)
  In the theory of stochastic processes, the filtering problem is a mathematical model for a number of filtering problems in signal processing and the like. The general idea is to form some kind of "best estimate" for the true value of some system, given only some observations of that system...
  
  
• Financial econometrics
  Financial econometrics
  People working in the finance industry often use econometric techniques in a range of activities. For example in support of portfolio management, risk management and in the analysis of securities...
  
  
• Financial models with long-tailed distributions and volatility clustering
• Finite-dimensional distribution
  Finite-dimensional distribution
  In mathematics, finite-dimensional distributions are a tool in the study of measures and stochastic processes. A lot of information can be gained by studying the "projection" of a measure onto a finite-dimensional vector space .-Finite-dimensional distributions of a measure:Let be a measure space...
  
  
• First-hitting-time model
  First-hitting-time model
  In statistics, first-hitting-time models are a sub-class of survival models. The first hitting time, also called first passage time, of a set A with respect to an instance of a stochastic process is the time until the stochastic process first enters A....
  
  
• First-in-man study
  First-in-man study
  A first-in-man study is a clinical trial where a medical procedure, previously developed and assessed through in vitro or animal testing, or through mathematical modelling is tested on human subjects for the first time....
  
  
• Fishburn–Shepp inequality
  Fishburn–Shepp inequality
  In combinatorial mathematics, the Fishburn–Shepp inequality is an inequality for the number of extensions of partial orders to linear orders, found by and .It states that if x, y, and z are incomparable elements of a finite poset, then PIn combinatorial mathematics, the Fishburn–Shepp inequality...
  
  
• Fisher consistency
  Fisher consistency
  In statistics, Fisher consistency, named after Ronald Fisher, is a desirable property of an estimator asserting that if the estimator were calculated using the entire population rather than a sample, the true value of the estimated parameter would be obtained...
  
  
• Fisher information
  Fisher information
  In mathematical statistics and information theory, the Fisher information is the variance of the score. In Bayesian statistics, the asymptotic distribution of the posterior mode depends on the Fisher information and not on the prior...
  
  
• Fisher information metric
  Fisher information metric
  In information geometry, the Fisher information metric is a particular Riemannian metric which can be defined on a smooth statistical manifold, i.e., a smooth manifold whose points are probability measures defined on a common probability space....
  
  
• Fisher kernel
  Fisher kernel
  In statistical classification, the Fisher kernel, named in honour of Sir Ronald Fisher, is a function that measures the similarity of two objects on the basis of sets of measurements for each object and a statistical model...
  
  
• Fisher transformation
  Fisher transformation
  In statistics, hypotheses about the value of the population correlation coefficient ρ between variables X and Y can be tested using the Fisher transformation applied to the sample correlation coefficient r.-Definition:...
  
  
• Fisher's exact test
  Fisher's exact test
  Fisher's exact test is a statistical significance test used in the analysis of contingency tables where sample sizes are small. It is named after its inventor, R. A...
  
  
• Fisher's inequality
  Fisher's inequality
  In combinatorial mathematics, Fisher's inequality, named after Ronald Fisher, is a necessary condition for the existence of a balanced incomplete block design satisfying certain prescribed conditions....
  
  
• Fisher's linear discriminator
• Fisher's method
  Fisher's Method
  In statistics, Fisher's method, also known as Fisher's combined probability test, is a technique for data fusion or "meta-analysis" . It was developed by and named for Ronald Fisher...
  
  
• Fisher's noncentral hypergeometric distribution
  Fisher's noncentral hypergeometric distribution
  In probability theory and statistics, Fisher's noncentral hypergeometric distribution is a generalization of the hypergeometric distribution where sampling probabilities are modified by weight factors...
  
  
• Fisher's z-distribution
• Fisher-Tippett distribution — redirects to Generalized extreme value distribution
• Fisher–Tippett–Gnedenko theorem
• Five-number summary
  Five-number summary
  The five-number summary is a descriptive statistic that provides information about a set of observations. It consists of the five most important sample percentiles:# the sample minimum # the lower quartile or first quartile...
  
  
• Fixed effects estimator
  Fixed effects estimator
  In econometrics and statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random. This is in contrast to random effects models and mixed models in which either all or some of...
  
   and Fixed effects estimation — redirect to Fixed effects model
• FLAME clustering
  FLAME clustering
  Fuzzy clustering by Local Approximation of MEmberships is a data clustering algorithm that defines clusters in the dense parts of a dataset and performs cluster assignment solely based on the neighborhood relationships among objects...
  
  
• Fleiss' kappa
  Fleiss' kappa
  Fleiss' kappa is a statistical measure for assessing the reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items or classifying items. This contrasts with other kappas such as Cohen's kappa, which only work when assessing the agreement...
  
  
• Fleming-Viot process
  Fleming-Viot process
  In probability theory, a Fleming–Viot process is a member of a particular subset of probability-measure valued Markov processes on compact metric spaces, as defined in the 1979 paper by Wendell Helms Fleming and Michel Viot...
  
  
• Flood risk assessment
  Flood risk assessment
  A flood risk assessment is an assessment of the risk of flooding, particularly in relation to residential, commercial and industrial land use.-England and Wales:...
  
  
• Floor effect
  Floor effect
  In statistics, the term floor effect refers to when data cannot take on a value lower than some particular number, called the floor.An example of this is when an IQ test is given to young children who have either been given training or have been given no training...
  
  
• FNN algorithm
  FNN algorithm
  The false nearest neighbor algorithm is an algorithm for estimating the embedding dimension....
  
    (false nearest neighbour algorithm)
• Focused information criterion
  Focused information criterion
  In statistics, the focused information criterion is a method for selecting the most appropriate model among a set of competitors for a given data set...
  
  
• Fokker–Planck equation
• Folded normal distribution
  Folded Normal Distribution
  The folded normal distribution is a probability distribution related to the normal distribution. Given a normally distributed random variable X with mean μ and variance σ2, the random variable Y = |X| has a folded normal distribution. Such a case may be encountered if only the magnitude of some...
  
  
• Forecast bias
  Forecast bias
  A forecast bias occurs when there are consistent differences between actual outcomes and previously generated forecasts of those quantities; that is, forecasts may have a general tendency to be too high or too low...
  
  
• Forecast error
  Forecast error
  In statistics, a forecast error is the difference between the actual or real and the predicted or forecast value of a time series or any other phenomenon of interest....
  
  
• Forecast skill
  Forecast skill
  Skill in forecasting is a scaled representation of forecast error that relates the forecast accuracy of a particular forecast model to some reference model....
  
  
• Forecasting
  Forecasting
  Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. A commonplace example might be estimation for some variable of interest at some specified future date. Prediction is a similar, but more general term...
  
  
• Forest plot
  Forest plot
  A forest plot is a graphical display designed to illustrate the relative strength of treatment effects in multiple quantitative scientific studies addressing the same question. It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of...
  
  
• Fork-join queue
  Fork-join queue
  In queueing theory, a discipline within the mathematical theory of probability, a fork-join queue is a queue where incoming jobs are split on arrival for service by numerous servers and joined before departure. The model is often used for parallel computations or systems where products need to be...
  
  
• Formation matrix
  Formation matrix
  In statistics and information theory, the expected formation matrix of a likelihood function L is the matrix inverse of the Fisher information matrix of L, while the observed formation matrix of L is the inverse of the observed information matrix of L.Currently, no notation for dealing with...
  
  
• Forward measure
  Forward measure
  In finance, a T-forward measure is a pricing measure absolutely continuous with respect to a risk-neutral measure but rather than using the money market as numeraire, it uses a bond with maturity T...
  
  
• Foster's theorem
  Foster's theorem
  In probability theory, Foster's theorem, named after F. G. Foster, is used to draw conclusions about the positive recurrence of Markov chains with countable state spaces...
  
  
• Foundations of statistics
  Foundations of statistics
  Foundations of statistics is the usual name for the epistemological debate in statistics over how one should conduct inductive inference from data...
  
  
• Founders of statistics
  Founders of statistics
  Statistics is the theory and application of mathematics to the scientific method including hypothesis generation, experimental design, sampling, data collection, data summarization, estimation, prediction and inference from those results to the population from which the experimental sample was drawn...
  
  
• Fourier analysis
• Fraction of variance unexplained
  Fraction of variance unexplained
  In statistics, the fraction of variance unexplained in the context of a regression task is the fraction of variance of the regressand Y which cannot be explained, i.e., which is not correctly predicted, by the explanatory variables X....
  
  
• Fractional Brownian motion
• Fractional factorial design
• Fréchet distribution
• Fréchet mean
  Fréchet mean
  The Fréchet mean , is the point, x, that minimizes the Fréchet function, in cases where such a unique minimizer exists. The value at a point p, of the Fréchet function associated to a random point X on a complete metric space is the expected squared distance from p to X...
  
  
• Free statistical software
  Free statistical software
  In this article, the word free generally means can be legally obtained without paying any money . Just a few of the software packages mentioned here are also free as in the sense of free speech: they are not only open source but also free software in the sense that the source code of the software...
  
  
• Freedman's paradox
  Freedman's paradox
  In statistical analysis, Freedman's paradox, named after David Freedman, describes a problem in model selection whereby predictor variables with no explanatory power can appear artificially important. Freedman demonstrated that this is a common occurrence when the number of variables is similar to...
  
  
• Freedman–Diaconis rule
• Freidlin–Wentzell theorem
• Frequency (statistics)
  Frequency (statistics)
  In statistics the frequency of an event i is the number ni of times the event occurred in the experiment or the study. These frequencies are often graphically represented in histograms....
  
  
• Frequency distribution
  Frequency distribution
  In statistics, a frequency distribution is an arrangement of the values that one or more variables take in a sample. Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of...
  
  
• Frequency domain
  Frequency domain
  In electronics, control systems engineering, and statistics, frequency domain is a term used to describe the domain for analysis of mathematical functions or signals with respect to frequency, rather than time....
  
  
• Frequency probability
  Frequency probability
  Frequency probability is the interpretation of probability that defines an event's probability as the limit of its relative frequency in a large number of trials. The development of the frequentist account was motivated by the problems and paradoxes of the previously dominant viewpoint, the...
  
  
• Frequentist inference
  Frequentist inference
  Frequentist inference is one of a number of possible ways of formulating generally applicable schemes for making statistical inferences: that is, for drawing conclusions from statistical samples. An alternative name is frequentist statistics...
  
  
• Friedman test
  Friedman test
  The Friedman test is a non-parametric statistical test developed by the U.S. economist Milton Friedman. Similar to the parametric repeated measures ANOVA, it is used to detect differences in treatments across multiple test attempts. The procedure involves ranking each row together, then...
  
  
• Friendship paradox
  Friendship paradox
  The friendship paradox is the phenomenon first observed by the sociologist Scott L. Feld in 1991 that most people have fewer friends than their friends have, on average. It can be explained as a form of sampling bias in which people with greater numbers of friends have an increased likelihood of...
  
  
• Frisch–Waugh–Lovell theorem
• Fully crossed design
• Function approximation
  Function approximation
  The need for function approximations arises in many branches of applied mathematics, and computer science in particular. In general, a function approximation problem asks us to select a function among a well-defined class that closely matches a target function in a task-specific way.One can...
  
  
• Functional data analysis
  Functional data analysis
  Functional data analysis is a branch of statistics that analyzes data providing information about curves, surfaces or anything else varying over a continuum...
  
  
• Funnel plot
  Funnel plot
  A funnel plot is a useful graph designed to check the existence of publication bias in systematic reviews and meta-analyses. It assumes that the largest studies will be near the average, and small studies will be spread on both sides of the average...
  
  
• Fuzzy logic
  Fuzzy logic
  Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1...
  
  
• Fuzzy measure theory
  Fuzzy measure theory
  Fuzzy measure theory considers a number of special classes of measures, each of which is characterized by a special property. Some of the measures used in this theory are plausibility and belief measures, fuzzy set membership function and the classical probability measures...
  
  
• FWL theorem
  FWL theorem
  In econometrics, the Frisch–Waugh–Lovell theorem is named after the econometricians Ragnar Frisch, Frederick V. Waugh, and Michael C. Lovell.The Frisch–Waugh–Lovell theorem states that if the regression we are concerned with is:...
  
   — relating regression and projection

G

• G-network
• G-test
  G-test
  In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended....
  
  
• Galbraith plot
  Galbraith plot
  In statistics, a Galbraith plot , is one way of displaying several estimates of the same quantity that have different standard errors....
  
  
• Gallagher Index
  Gallagher Index
  The Gallagher Index is used to measure the disproportionality of an electoral outcome, that is the difference between the percentage of votes received and the percentage of seats a party gets in the resulting legislature. This is especially useful for comparing proportionality across electoral...
  
  
• Galton–Watson process
• Galton's problem
  Galton's problem
  Galton’s problem, named after Sir Francis Galton, is the problem of drawing inferences from cross-cultural data, due to the statistical phenomenon now called autocorrelation. The problem is now recognized as a general one that applies to all nonexperimental studies and to experimental design as well...
  
  
• Gambler's fallacy
  Gambler's fallacy
  The Gambler's fallacy, also known as the Monte Carlo fallacy , and also referred to as the fallacy of the maturity of chances, is the belief that if deviations from expected behaviour are observed in repeated independent trials of some random process, future deviations in the opposite direction are...
  
  
• Gambler's ruin
  Gambler's ruin
  The term gambler's ruin is used for a number of related statistical ideas:* The original meaning is that a gambler who raises his bet to a fixed fraction of bankroll when he wins, but does not reduce it when he loses, will eventually go broke, even if he has a positive expected value on each bet.*...
  
  
• Gambling and information theory
  Gambling and information theory
  Statistical inference might be thought of as gambling theory applied to the world around. The myriad applications for logarithmic information measures tell us precisely how to take the best guess in the face of partial information. In that sense, information theory might be considered a formal...
  
  
• Game of chance
  Game of chance
  A game of chance is a game whose outcome is strongly influenced by some randomizing device, and upon which contestants may or may not wager money or anything of monetary value...
  
  
• Gamma distribution
• Gamma test (statistics)
  Gamma test (statistics)
  In statistics, a gamma test tests the strength of association of the cross tabulated data when both variables are measured at the ordinal level. It makes no adjustment for either table size or ties. Values range from −1 to +1...
  
  
• Gamma process
• Gamma variate
• GAUSS (software)
  GAUSS (software)
  GAUSS is a matrix programming language for mathematics and statistics, developed and marketed by Aptech Systems. Its primary purpose is the solution of numerical problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization...
  
  
• Gauss's inequality
  Gauss's inequality
  In probability theory, Gauss's inequality gives an upper bound on the probability that a unimodal random variable lies more than any given distance from its mode....
  
  
• Gauss–Kuzmin distribution
• Gauss–Markov process
  Gauss–Markov process
  Gauss–Markov stochastic processes are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes. The stationary Gauss–Markov process is a very special case because it is unique, except for some trivial exceptions...
  
  
• Gauss–Markov theorem
  Gauss–Markov theorem
  In statistics, the Gauss–Markov theorem, named after Carl Friedrich Gauss and Andrey Markov, states that in a linear regression model in which the errors have expectation zero and are uncorrelated and have equal variances, the best linear unbiased estimator of the coefficients is given by the...
  
  
• Gauss–Newton algorithm
• Gaussian function
• Gaussian isoperimetric inequality
• Gaussian measure
  Gaussian measure
  In mathematics, Gaussian measure is a Borel measure on finite-dimensional Euclidean space Rn, closely related to the normal distribution in statistics. There is also a generalization to infinite-dimensional spaces...
  
  
• Gaussian noise
  Gaussian noise
  Gaussian noise is statistical noise that has its probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. In other words, the values that the noise can take on are Gaussian-distributed. A special case is white Gaussian noise, in which...
  
  
• Gaussian process
  Gaussian process
  In probability theory and statistics, a Gaussian process is a stochastic process whose realisations consist of random values associated with every point in a range of times such that each such random variable has a normal distribution...
  
  
• Gaussian process emulator
  Gaussian process emulator
  In statistics, Gaussian process emulator is one name for a general type of statistical model that has been used in contexts where the problem is to make maximum use of the outputs of a complicated computer-based simulation model. Each run of the simulation model is computationally expensive and...
  
  
• Gaussian q-distribution
  Gaussian q-distribution
  In mathematical physics and probability and statistics, the Gaussian q-distribution is a family of probability distributions that includes, as limiting cases, the uniform distribution and the normal distribution...
  
  
• Geary's C
  Geary's C
  Geary's C is a measure of spatial autocorrelation. Like autocorrelation, spatial autocorrelation means that adjacent observations of the same phenomenon are correlated. However, autocorrelation is about proximity in time. Spatial autocorrelation is about proximity in space...
  
  
• GEH
  GEH
  The GEH Statistic is a formula used in traffic engineering, traffic forecasting, and traffic modelling to compare two sets of traffic volumes. The GEH formula gets its name from Geoffrey E. Havers, who invented it in the 1970s while working as a transport planner in London, England. Although its...
  
   — a statistic comparing modelled and observed counts
• General linear model
  General linear model
  The general linear model is a statistical linear model.It may be written aswhere Y is a matrix with series of multivariate measurements, X is a matrix that might be a design matrix, B is a matrix containing parameters that are usually to be estimated and U is a matrix containing errors or...
  
  
• General matrix notation of a VAR(p)
• Generalizability theory
  Generalizability theory
  Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability of measurements under specific conditions. It is particularly useful for assessing the reliability of performance...
  
  
• Generalized additive model
  Generalized additive model
  In statistics, the generalized additive model is a statistical model developed by Trevor Hastie and Rob Tibshirani for blending properties of generalized linear models with additive models....
  
  
• Generalized additive model for location, scale and shape
  Generalized additive model for location, scale and shape
  In statistics, the generalized additive model location, scale and shape is a class of statistical model that provides extended capabilities compared to the simpler generalized linear models and generalized additive models. These simpler models allow the typical values of a quantity being modelled...
  
  
• Generalized canonical correlation
  Generalized canonical correlation
  In statistics, the generalized canonical correlation analysis , is a way of making sense of cross-correlation matrices between the sets of random variables when there are more than two sets. While a conventional CCA generalizes Principal component analysis to two sets of random variables, a gCCA ...
  
  
• Generalized chi-squared distribution
• Generalized Dirichlet distribution
  Generalized Dirichlet distribution
  In statistics, the generalized Dirichlet distribution is a generalization of the Dirichlet distribution with a more general covariance structure and twice the number of parameters...
  
  
• Generalized entropy index
  Generalized entropy index
  The generalized entropy index is a general formula for measuring redundancy in data. The redundancy can be viewed as inequality, lack of diversity, non-randomness, compressibility, or segregation in the data. The primary use is for income inequality...
  
  
• Generalized estimating equation
• Generalized expected utility
  Generalized expected utility
  The expected utility model developed by John von Neumann and Oskar Morgenstern dominated decision theory from its formulation in 1944 until the late 1970s, not only as a prescriptive, but also as a descriptive model, despite powerful criticism from Maurice Allais and Daniel Ellsberg who showed...
  
  
• Generalized extreme value distribution
• Generalized gamma distribution
  Generalized gamma distribution
  The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution...
  
  
• Generalized Gaussian distribution
• Generalised hyperbolic distribution
• Generalized inverse Gaussian distribution
• Generalized least squares
  Generalized least squares
  In statistics, generalized least squares is a technique for estimating the unknown parameters in a linear regression model. The GLS is applied when the variances of the observations are unequal , or when there is a certain degree of correlation between the observations...
  
  
• Generalized linear array model
  Generalized linear array model
  In statistics, the generalized linear array model is used for analyzing data sets with array structures. It based on the generalized linear model with the design matrix written as a Kronecker product.- Overview :...
  
  
• Generalized linear mixed model
  Generalized linear mixed model
  In statistics, a generalized linear mixed model is a particular type of mixed model. It is an extension to the generalized linear model in which the linear predictor contains random effects in addition to the usual fixed effects...
  
  
• Generalized linear model
  Generalized linear model
  In statistics, the generalized linear model is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to...
  
  
• Generalized logistic distribution
  Generalized logistic distribution
  The term generalized logistic distribution is used as the name for several different families of probability distributions. For example, Johnson et al. list four forms, which are listed below. One family described here has also been called the skew-logistic distribution...
  
  
• Generalized method of moments
  Generalized method of moments
  In econometrics, generalized method of moments is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data...
  
  
• Generalized multidimensional scaling
• Generalized normal distribution
• Generalized p-value
  Generalized p-value
  In statistics, a generalized p-value is an extended version of the classical p-value, which except in a limited number of applications, provide only approximate solutions....
  
  
• Generalized Pareto distribution
• Generalized Procrustes analysis
  Generalized Procrustes analysis
  Generalized Procrustes analysis is a method of statistical analysis that can be used to compare the shapes of objects, or the results of surveys, interviews, panels. It was developed for analyising the results of free-choice profiling, a survey technique which allows respondents to describe a...
  
  
• Generalized randomized block design
  Generalized randomized block design
  In randomized statistical experiments, generalized randomized block designs are used to study the interaction between blocks and treatments...
  
  
• Generalized Tobit
  Generalized Tobit
  A generalized Tobit is a generalization of the econometric Tobit model after James Tobin. It is also called Heckit after James Heckman. Anothername is "type 2 Tobit model".Tobit models assume that a variable is truncated.-References:...
  
  
• Generalized Wiener process
  Generalized Wiener process
  In statistics, a generalized Wiener process is a continuous time random walk with drift and random jumps at every point in time...
  
  
• Generative model
  Generative model
  In probability and statistics, a generative model is a model for randomly generating observable data, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences...
  
  
• Genetic epidemiology
  Genetic epidemiology
  Genetic epidemiology is the study of the role of genetic factors in determining health and disease in families and in populations, and the interplay of such genetic factors with environmental factors...
  
  
• GenStat
  GenStat
  GenStat is a general statistical package. Early versions were developed for large mainframe computers. Up until version 5, there was a Unix binary available, and this continues to be used by many universities and research institutions...
  
   – software
• Geo-imputation
  Geo-imputation
  In data analysis involving geographical locations, geo-imputation or geographical imputation methods are steps taken to replace missing values for exact locations with approximate locations derived from associated data...
  
  
• Geodemographic segmentation
  Geodemographic Segmentation
  In marketing, Geodemographic segmentation is a multivariate statistical classification technique for discovering whether the individuals of a population fall into different groups by making quantitative comparisons of multiple characteristics with the assumption that the differences within any...
  
  
• Geometric Brownian motion
  Geometric Brownian motion
  A geometric Brownian motion is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion, also called a Wiener process...
  
  
• Geometric data analysis
  Geometric data analysis
  Geometric data analysis can refer to geometric aspects of image analysis, pattern analysis and shape analysis or the approach of multivariate statistics that treats arbitrary data sets as clouds of points in n-dimensional space...
  
  
• Geometric distribution
• Geometric median
  Geometric median
  The geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points. This generalizes the median, which has the property of minimizing the sum of distances for one-dimensional data, and provides a central tendency in higher...
  
  
• Geometric standard deviation
  Geometric standard deviation
  In probability theory and statistics, the geometric standard deviation describes how spread out are a set of numbers whose preferred average is the geometric mean...
  
  
• Geometric stable distribution
• Geospatial predictive modeling
  Geospatial predictive modeling
  Geospatial predictive modeling is conceptually rooted in the principle that the occurrences ofevents being modeled are limited in distribution...
  
  
• Geostatistics
  Geostatistics
  Geostatistics is a branch of statistics focusing on spatial or spatiotemporal datasets. Developed originally to predict probability distributions of ore grades for mining operations, it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology,...
  
  
• German tank problem
  German tank problem
  In the statistical theory of estimation, estimating the maximum of a uniform distribution is a common illustration of differences between estimation methods...
  
  
• Gerschenkron effect
  Gerschenkron effect
  The Gerschenkron effect was developed by Alexander Gerschenkron, and claims that changing the base year for an index determines the growth rate of the index.This description is from the OECD website :...
  
  
• Gibbs sampling
  Gibbs sampling
  In statistics and in statistical physics, Gibbs sampling or a Gibbs sampler is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables...
  
  
• Gillespie algorithm
  Gillespie algorithm
  In probability theory, the Gillespie algorithm generates a statistically correct trajectory of a stochastic equation. It was created by Joseph L...
  
  
• Gini coefficient
  Gini coefficient
  The Gini coefficient is a measure of statistical dispersion developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper "Variability and Mutability" ....
  
  
• Girsanov theorem
  Girsanov theorem
  In probability theory, the Girsanov theorem describes how the dynamics of stochastic processes change when the original measure is changed to an equivalent probability measure...
  
  
• Gittins index
  Gittins index
  The Gittins index is a measure of the reward that can be achieved by a process evolving from its present state onwards with the probability that it will be terminated in future...
  
  
• GLIM (software)
  GLIM (software)
  GLIM is a statistical software program for fitting generalized linear models .It was developed by the Royal Statistical Society'sWorking Party on Statistical Computing...
  
   – software
• Glivenko–Cantelli theorem
• GLUE (uncertainty assessment)
  GLUE (uncertainty assessment)
  In hydrology, Generalized Likelihood Uncertainty Estimation is a statistical method for quantifying the uncertainty of model predictions. The method has been introduced by Beven and Binley...
  
  
• Goldfeld–Quandt test
  Goldfeld–Quandt test
  In statistics, the Goldfeld–Quandt test checks for homoscedasticity in regression analyses. It does this by dividing a dataset into two parts or groups, and hence the test is sometimes called a two-group test. The Goldfeld–Quandt test is one of two tests proposed in a 1965 paper by Stephen...
  
  
• Gompertz function
• Gompertz–Makeham law of mortality
• Good–Turing frequency estimation
• Goodhart's law
  Goodhart's law
  Goodhart's law, although it can be expressed in many ways, states that once a social or economic indicator or other surrogate measure is made a target for the purpose of conducting social or economic policy, then it will lose the information content that would qualify it to play that role...
  
  
• Goodman and Kruskal's lambda
  Goodman and Kruskal's lambda
  In probability theory and statistics, Goodman & Kruskal's lambda is a measure of proportional reduction in error in cross tabulation analysis...
  
  
• Goodness of fit
  Goodness of fit
  The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g...
  
  
• Gordon–Newell network
• Gordon–Newell theorem
  Gordon–Newell theorem
  In queueing theory, a discipline within the mathematical theory of probability, the Gordon–Newell theorem is an extension of Jackson's theorem from open queueing networks to closed queueing networks of exponential servers. We cannot apply Jackson's theorem to closed networks because the queue...
  
  
• Graeco-Latin square
  Graeco-Latin square
  In mathematics, a Graeco-Latin square or Euler square or orthogonal Latin squares of order n over two sets S and T, each consisting of n symbols, is an n×n arrangement of cells, each cell containing an ordered pair , where s is in S and t is in T, such that every row and every column contains...
  
  
• Grand mean
  Grand mean
  The grand mean is the mean of the means of several subsamples. For example, consider several lots, each containing several items. The items from each lot are sampled for a measure of some variable and the means of the measurements from each lot are computed. The mean of the measures from each lot...
  
  
• Granger causality
  Granger causality
  The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. Ordinarily, regressions reflect "mere" correlations, but Clive Granger, who won a Nobel Prize in Economics, argued that there is an interpretation of a set of tests...
  
  
• Graph cuts in computer vision
  Graph cuts in computer vision
  As applied in the field of computer vision, graph cuts can be employed to efficiently solve a wide variety of low-level computer vision problems , such as image smoothing, the stereo correspondence problem, and many other computer vision problems that can be formulated in terms of energy minimization...
  
   – a potential application of Bayesian analysis
• Graphical model
  Graphical model
  A graphical model is a probabilistic model for which a graph denotes the conditional independence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning....
  
  
• Graphical models for protein structure
  Graphical models for protein structure
  Graphical models have become powerful frameworks for protein structure prediction, protein–protein interaction and free energy calculations for protein structures...
  
  
• GraphPad InStat
  GraphPad InStat
  GraphPad InStat is a commercial scientific statistics software published by GraphPad Software, Inc., a privately owned California corporation. InStat is available for both Windows and Macintosh computers.-Features:...
  
   – software
• GraphPad Prism
  GraphPad Prism
  GraphPad Prism is a commercial scientific 2D graphing and statistics software published by GraphPad Software, Inc., a privately-held California corporation...
  
   – software
• Gravity model of trade
  Gravity model of trade
  The gravity model of trade in international economics, similar to other gravity models in social science, predicts bilateral trade flows based on the economic sizes of and distance between two units. The model was first used by Tinbergen in 1962...
  
  
• Greenwood statistic
• Gretl
  Gretl
  gretl is an open-source statistical package, mainly for econometrics. The name is an acronym for Gnu Regression, Econometrics and Time-series Library. It has a graphical user interface and can be used together with X-12-ARIMA, TRAMO/SEATS, R, Octave, and Ox. It is written in C, uses GTK as widget...
  
  
• Group family
  Group family
  In probability theory, especially as that field is used in statistics, a group family of probability distributions is a family obtained by subjecting a random variable with a fixed distribution to a suitable family of transformations such as a location-scale family, or otherwise a family of...
  
  
• Group method of data handling
  Group method of data handling
  Group method of data handling is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models....
  
  
• Group size measures
  Group size measures
  Many animals, including humans, tend to live in groups, herds, flocks, bands, packs, shoals, or colonies of conspecific individuals. The size of these groups, as expressed by the number of participant individuals, is an important aspect of their social environment...
  
  
• Grouped data
  Grouped data
  Grouped data is a statistical term used in data analysis. A raw dataset can be organized by constructing a table showing the frequency distribution of the variable...
  
  
• Grubbs' test for outliers
  Grubbs' test for outliers
  Grubbs' test , also known as the maximum normed residual test, is a statistical test used to detect outliers in a univariate data set assumed to come from a normally distributed population.-Definition:...
  
  
• Guess value
  Guess value
  A guess value is more commonly called a starting value or initial value. These are necessary for most optimization problems which use search algorithms, because those algorithms are mainly deterministic and iterative, and they need to start somewhere...
  
  
• Guesstimate
  Guesstimate
  Guesstimate is an informal English contraction of guess and estimate, first used by American statisticians in 1934 or 1935. It is defined as an estimate made without using adequate or complete information, or, more strongly, as an estimate arrived at by guesswork or conjecture...
  
  
• Gumbel distribution
• Guttman scale
  Guttman scale
  In statistical surveys conducted by means of structured interviews or questionnaires, a subset of the survey items having binary answers forms a Guttman scale if they can be ranked in some order so that, for a rational respondent, the response pattern can be captured by a single index on that...
  
  
• Gy's sampling theory
  Gy's sampling theory
  Gy's sampling theory is a theory about the sampling of materials, developed by Pierre Gy from the 1950s to beginning 2000s in articles and books including:* Sampling nomogram* Sampling of particulate materials; theory and practice...
  
  

H

• h-index
  H-index
  The h-index is an index that attempts to measure both the productivity and impact of the published work of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications...
  
  
• Hájek–Le Cam convolution theorem
  Hájek–Le Cam convolution theorem
  In statistics, the Hájek–Le Cam convolution theorem states that any regular estimator in a parametric model is asymptotically equivalent to a sum of two independent random variables, one of which is normal with asymptotic variance equal to the inverse of Fisher information, and the other having...
  
  
• Half circle distribution
• Half-logistic distribution
• Half-normal distribution
  Half-normal distribution
  The half-normal distribution is the probability distribution of the absolute value of a random variable that is normally distributed with expected value 0 and variance σ2. I.e...
  
  
• Halton sequence
• Hamburger moment problem
• Hannan–Quinn information criterion
• Harris chain
• Hardy–Weinberg principle – statistical genetics
• Hartley's test
  Hartley's test
  In statistics, Hartley's test, also known as the Fmax test or Hartley's Fmax, is used in the analysis of variance to verify that different groups have a similar variance, an assumption needed for other statistical tests.It was developed by H. O...
  
  
• Hat matrix
  Hat matrix
  In statistics, the hat matrix, H, maps the vector of observed values to the vector of fitted values. It describes the influence each observed value has on each fitted value...
  
  
• Hammersley–Clifford theorem
  Hammersley–Clifford theorem
  The Hammersley–Clifford theorem is a result in probability theory, mathematical statistics and statistical mechanics, that gives necessary and sufficient conditions under which a positive probability distribution can be represented as a Markov network...
  
  
• Hausdorff moment problem
• Hausman specification test redirects to Hausman test
  Hausman test
  The Hausman test or Hausman specification test is a statistical test in econometrics named after Jerry A. Hausman. The test evaluates the significance of an estimator versus an alternative estimator...
  
  
• Haybittle–Peto boundary
  Haybittle–Peto boundary
  The Haybittle–Peto boundary is a rule for deciding when to stop a clinical trial prematurely.The typical clinical trial compares two groups of patients. One group are given a placebo or conventional treatment, while the other group of patients are given the treatment that is being tested...
  
  
• Hazard function — redirects to Failure rate
  Failure rate
  Failure rate is the frequency with which an engineered system or component fails, expressed for example in failures per hour. It is often denoted by the Greek letter λ and is important in reliability engineering....
  
  
• Hazard ratio
  Hazard ratio
  In survival analysis, the hazard ratio is the ratio of the hazard rates corresponding to the conditions described by two sets of explanatory variables. For example, in a drug study, the treated population may die at twice the rate per unit time as the control population. The hazard ratio would be...
  
  
• Heaps' law
  Heaps' law
  In linguistics, Heaps' law is an empirical law which describes the portion of a vocabulary which is represented by an instance document consisting of words chosen from the vocabulary. This can be formulated as V_R = Kn^\beta...
  
  
• Health care analytics
  Health care analytics
  Health care analytics is a rapidly evolving field of health care business solutions that makes extensive use of data, statistical and qualitative analysis, explanatory and predictive modeling.- Theory :...
  
  
• Heart rate variability
  Heart rate variability
  Heart rate variability is a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval....
  
  
• Heavy-tailed distribution
  Heavy-tailed distribution
  In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution...
  
  
• Heckman correction
  Heckman correction
  The Heckman correction is any of a number of related statistical methods developed by James Heckman in 1976 through 1979 which allow the researcher to correct for selection bias...
  
  
• Hedonic regression
  Hedonic regression
  In economics, hedonic regression or hedonic demand theory is a revealed preference method of estimating demand or value. It decomposes the item being researched into its constituent characteristics, and obtains estimates of the contributory value of each characteristic...
  
  
• Hellin's law
  Hellin's Law
  Hellin's Law is the principle that one in about 89 pregnancies ends in the birth of twins, triplets once in 892 births, and quadruplets once in 893 births....
  
  
• Hellinger distance
  Hellinger distance
  In probability and statistics, the Hellinger distance is used to quantify the similarity between two probability distributions. It is a type of f-divergence...
  
  
• Helmert–Wolf blocking
• Herfindahl index
  Herfindahl index
  The Herfindahl index is a measure of the size of firms in relation to the industry and an indicator of the amount of competition among them. Named after economists Orris C. Herfindahl and Albert O. Hirschman, it is an economic concept widely applied in competition law, antitrust and also...
  
  
• Heston model
  Heston model
  In finance, the Heston model, named after Steven Heston, is a mathematical model describing the evolution of the volatility of an underlying asset...
  
  
• Heteroscedasticity
• Heteroscedasticity-consistent standard errors
  Heteroscedasticity-consistent standard errors
  The topic of heteroscedasticity-consistent standard errors arises in statistics and econometrics in the context of linear regression and also time series analysis...
  
  
• Heteroskedasticity — redirects to Heteroscedasticity
• Hidden Markov model
  Hidden Markov model
  A hidden Markov model is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved states. An HMM can be considered as the simplest dynamic Bayesian network. The mathematics behind the HMM was developed by L. E...
  
  
• Hidden Markov random field
  Hidden Markov random field
  A hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields have an underlying Markov random field.Suppose that we observe a random variable Y_i , where i \in S ....
  
  
• Hidden semi-Markov model
  Hidden semi-Markov model
  A hidden semi-Markov model is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the probability of there being a change in the hidden state depends on the amount of time that has elapsed...
  
  
• Hierarchical Bayes model
  Hierarchical Bayes model
  The hierarchical Bayes model is a method in modern Bayesian statistical inference. It is a framework for describing statistical models that can capture dependencies more realistically than non-hierarchical models....
  
  
• Hierarchical clustering
  Hierarchical clustering
  In statistics, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:...
  
  
• Hierarchical hidden Markov model
  Hierarchical hidden Markov model
  The hierarchical hidden Markov model is a statistical model derived from the hidden Markov model . In an HHMM each state is considered to be a self-contained probabilistic model. More precisely each state of the HHMM is itself an HHMM....
  
  
• Hierarchical linear modeling
  Hierarchical linear modeling
  In statistics, hierarchical linear modeling , a form of multi-level analysis, is a more advanced form of simple linear regression and multiple linear regression. Multilevel analysis allows variance in outcome variables to be analysed at multiple hierarchical levels, whereas in simple linear and...
  
  
• High-dimensional statistics
  High-dimensional statistics
  In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than dimensions considered in classical multivariate analysis. High-dimensional statistics relies on the theory of random vectors...
  
  
• Higher-order factor analysis
  Higher-order factor analysis
  Higher-order factor analysis is a statistical method consisting of repeating steps factor analysis – oblique rotation – factor analysis of rotated factors... Its merit is to enable the researcher to see the hierarchical structure of studied phenomena...
  
  
• Higher-order statistics
  Higher-order statistics
  Higher-order statistics are descriptive measures of, among other things, qualities of probability distributions and sample distributions, and are, themselves, extensions of first- and second-order measures to higher orders. Skewness and kurtosis are examples of this...
  
  
• Hirschman uncertainty
  Hirschman uncertainty
  In quantum mechanics, information theory, and Fourier analysis, the Hirschman uncertainty is defined as the sum of the temporal and spectral Shannon entropies. It turns out that Heisenberg's uncertainty principle can be expressed as a lower bound on the sum of these entropies...
  
  
• Histogram
  Histogram
  In statistics, a histogram is a graphical representation showing a visual impression of the distribution of data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Karl Pearson...
  
  
• Historiometry
  Historiometry
  Historiometry is the historical study of human progress or individual personal characteristics, using statistics to analyze references to geniuses, their statements, behavior and discoveries in relatively neutral texts...
  
  
• History of randomness
  History of randomness
  In ancient history, the concepts of chance and randomness were intertwined with that of fate. Many ancient peoples threw dice to determine fate, and this later evolved into games of chance...
  
  
• History of statistics
  History of statistics
  The history of statistics can be said to start around 1749 although, over time, there have been changes to the interpretation of what the word statistics means. In early times, the meaning was restricted to information about states...
  
  
• Hitting time
  Hitting time
  In the study of stochastic processes in mathematics, a hitting time is a particular instance of a stopping time, the first time at which a given process "hits" a given subset of the state space...
  
  
• Hodges’ estimator
  Hodges’ estimator
  In statistics, Hodges’ estimator is a famous counter example of an estimator which is "superefficient", i.e. it attains smaller asymptotic variance than regular efficient estimators...
  
  
• Hodges–Lehmann estimator
• Hoeffding's independence test
• Hoeffding's lemma
  Hoeffding's lemma
  In probability theory, Hoeffding's lemma is an inequality that bounds the moment-generating function of any bounded random variable. It is named after the Finnish–American mathematical statistician Wassily Hoeffding....
  
  
• Hoeffding's inequality
  Hoeffding's inequality
  In probability theory, Hoeffding's inequality provides an upper bound on the probability for the sum of random variables to deviate from its expected value. Hoeffding's inequality was proved by Wassily Hoeffding.LetX_1, \dots, X_n \!...
  
  
• Holm–Bonferroni method
• Holtsmark distribution
  Holtsmark distribution
  The Holtsmark distribution is a continuous probability distribution. The Holtsmark distribution is a special case of a stable distribution with the index of stability or shape parameter \alpha equal to 3/2 and skewness parameter \beta of zero. Since \beta equals zero, the distribution is...
  
  
• Homogeneity (statistics)
  Homogeneity (statistics)
  In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part...
  
  
• Homoscedasticity
  Homoscedasticity
  In statistics, a sequence or a vector of random variables is homoscedastic if all random variables in the sequence or vector have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity...
  
  
• Hoover index
• Horvitz–Thompson estimator
  Horvitz–Thompson estimator
  In statistics, the Horvitz–Thompson estimator, named after Daniel G. Horvitz and Donovan J. Thompson, is a method for estimating the mean of a superpopulation in a stratified sample. Inverse probability weighting is applied to account for different proportions of observations within strata...
  
  
• Hosmer–Lemeshow test
  Hosmer–Lemeshow test
  The Hosmer–Lemeshow test is a statistical test for goodness of fit for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population. The Hosmer–Lemeshow...
  
  
• Hotelling's T-squared distribution
• How to Lie with Statistics
  How to Lie with Statistics
  How to Lie with Statistics is a book written by Darrell Huff in 1954 presenting an introduction to statistics for the general reader. Huff was a journalist who wrote many "how to" articles as a freelancer, but was not a statistician....
  
   (book)
• Howland will forgery trial
  Howland will forgery trial
  The Howland will forgery trial was a U.S. court case in 1868 to decide Henrietta Howland Robinson's contest of the will of Sylvia Ann Howland. It is famous for the forensic use of mathematics by Benjamin Peirce as an expert witness.-History:...
  
  
• Hubbert curve
  Hubbert curve
  The Hubbert curve is an approximation of the production rate of a resource over time. It is a symmetric logistic distribution curve, often confused with the "normal" gaussian function. It first appeared in "Nuclear Energy and the Fossil Fuels," geophysicist M...
  
  
• Huber–White standard error — redirects to Heteroscedasticity-consistent standard errors
  Heteroscedasticity-consistent standard errors
  The topic of heteroscedasticity-consistent standard errors arises in statistics and econometrics in the context of linear regression and also time series analysis...
  
  
• Huber loss function
  Huber Loss Function
  In statistical theory, the Huber loss function is a function used in robust estimation that allows construction of an estimate which allows the effect of outliers to be reduced, while treating non-outliers in a more standard way.-Definition:...
  
  
• Human subject research
• Hurst exponent
  Hurst exponent
  The Hurst exponent is used as a measure of the long term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases....
  
  
• Hyper-exponential distribution
• Hyper-Graeco-Latin square design
  Hyper-Graeco-Latin square design
  In the design of experiments, hyper-Graeco-Latin squares are efficient designs to study the effect of one primary factor in the presence of 4 blocking factors. They are restricted, however, to the case in which all the factors have the same number of levels.Designs for 4- and 5-level factors are...
  
  
• Hyperbolic distribution
  Hyperbolic distribution
  The hyperbolic distribution is a continuous probability distribution that is characterized by the fact that the logarithm of the probability density function is a hyperbola. Thus the distribution decreases exponentially, which is more slowly than the normal distribution...
  
  
• Hyperbolic secant distribution
• Hypergeometric distribution
• Hyperparameter
  Hyperparameter
  In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis...
  
  
• Hyperprior
  Hyperprior
  In Bayesian statistics, a hyperprior is a prior distribution on a hyperparameter, that is, on a parameter of a prior distribution.As with the term hyperparameter, the use of hyper is to distinguish it from a prior distribution of a parameter of the model for the underlying system...
  
  
• Hypoexponential distribution
  Hypoexponential distribution
  In probability theory the hypoexponential distribution or the generalized Erlang distribution is a continuous distribution, that has found use in the same fields as the Erlang distribution, such as queueing theory, teletraffic engineering and more generally in stochastic processes...
  
  

I

• Idealised population
  Idealised population
  main article: effective population sizeIn population genetics an idealised population, also sometimes called a Fisher-Wright population after R.A. Fisher and Sewall Wright, is a population whose members can mate and reproduce with any other member of the other gender, has a sex ratio of 1 and no...
  
  
• Idempotent matrix
  Idempotent matrix
  In algebra, an idempotent matrix is a matrix which, when multiplied by itself, yields itself. That is, the matrix M is idempotent if and only if MM = M...
  
  
• Identifiability
  Identifiability
  In statistics, identifiability is a property which a model must satisfy in order for inference to be possible. We say that the model is identifiable if it is theoretically possible to learn the true value of this model’s underlying parameter after obtaining an infinite number of observations from it...
  
  
• Ignorability
  Ignorability
  In statistics, ignorability refers to an experiment design where the method of data collection do not depend on the missing data...
  
  
• Illustration of the central limit theorem
  Illustration of the central limit theorem
  This article gives two concrete illustrations of the central limit theorem. Both involve the sum of independent and identically-distributed random variables and show how the probability distribution of the sum approaches the normal distribution as the number of terms in the sum increases.The first...
  
  
• Image denoising
  Image denoising
  Image denoising refers to the recovery of a digital image that has been contaminated by additive white Gaussian noise .-Technical description:...
  
  
• Importance sampling
  Importance sampling
  In statistics, importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than the distribution of interest. It is related to Umbrella sampling in computational physics...
  
  
• Imprecise probability
  Imprecise probability
  Imprecise probability generalizes probability theory to allow for partial probability specifications, and is applicable when information is scarce, vague, or conflicting, in which case a unique probability distribution may be hard to identify...
  
  
• Imputation (statistics)
  Imputation (statistics)
  In statistics, imputation is the substitution of some value for a missing data point or a missing component of a data point. Once all missing values have been imputed, the dataset can then be analysed using standard techniques for complete data...
  
  
• Incidence (epidemiology)
  Incidence (epidemiology)
  Incidence is a measure of the risk of developing some new condition within a specified period of time. Although sometimes loosely expressed simply as the number of new cases during some time period, it is better expressed as a proportion or a rate with a denominator.Incidence proportion is the...
  
  
• Inclusion probability
  Inclusion probability
  In statistics, in the theory relating to sampling from finite populations, the inclusion probability of an element or member of the population is its probability of becoming part of the sample during the drawing of a single sample....
  
  
• Increasing process
• Indecomposable distribution
  Indecomposable distribution
  In probability theory, an indecomposable distribution is a probability distribution that cannot be represented as the distribution of the sum of two or more non-constant independent random variables: Z ≠ X + Y. If it can be so expressed, it is decomposable:...
  
  
• Independence of irrelevant alternatives
  Independence of irrelevant alternatives
  Independence of irrelevant alternatives is an axiom of decision theory and various social sciences.The word is used in different meanings in different contexts....
  
  
• Independent component analysis
  Independent component analysis
  Independent component analysis is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals...
  
  
• Independent and identically distributed random variables
  Independent and identically distributed random variables
  In probability theory and statistics, a sequence or other collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent....
  
  
• Index number
• Index of coincidence
  Index of coincidence
  In cryptography, coincidence counting is the technique of putting two texts side-by-side and counting the number of times that identical letters appear in the same position in both texts...
  
  
• Index of dispersion
• Indicators of spatial association
  Indicators of spatial association
  Indicators of spatial association are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable. For instance if we are studying cancer rates among census tracts in a given city local clusters in the rates mean that there are areas that have higher or lower...
  
  
• Indirect least squares
• Inductive inference
  Inductive inference
  Around 1960, Ray Solomonoff founded the theory of universal inductive inference, the theory of prediction based on observations; for example, predicting the next symbol based upon a given series of symbols...
  
  
• An inequality on location and scale parameters — redirects to Chebyshev's inequality
  Chebyshev's inequality
  In probability theory, Chebyshev’s inequality guarantees that in any data sample or probability distribution,"nearly all" values are close to the mean — the precise statement being that no more than 1/k2 of the distribution’s values can be more than k standard deviations away from the mean...
  
  
• Inference
  Inference
  Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.Human inference Inference is the act or process of deriving logical conclusions...
  
  
• Inferential statistics redirects to Statistical inference
  Statistical inference
  In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation...
  
  
• Infinite divisibility (probability)
  Infinite divisibility (probability)
  The concepts of infinite divisibility and the decomposition of distributions arise in probability and statistics in relation to seeking families of probability distributions that might be a natural choice in certain applications, in the same way that the normal distribution is...
  
  
• Infinite monkey theorem
  Infinite monkey theorem
  The infinite monkey theorem states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will almost surely type a given text, such as the complete works of William Shakespeare....
  
  
• Influence diagram
  Influence diagram
  An influence diagram is a compact graphical and mathematical representation of a decision situation...
  
  
• Info-gap decision theory
  Info-gap decision theory
  Info-gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportuneness for windfall – under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the...
  
  
• Information bottleneck method
  Information bottleneck method
  The information bottleneck method is a technique introduced by Naftali Tishby et al. [1] for finding the best tradeoff between accuracy and complexity when summarizing a random variable X, given a joint probability distribution between X and an observed relevant variable Y...
  
  
• Information geometry
  Information geometry
  Information geometry is a branch of mathematics that applies the techniques of differential geometry to the field of probability theory. It derives its name from the fact that the Fisher information is used as the Riemannian metric when considering the geometry of probability distribution families...
  
  
• Information gain ratio
  Information gain ratio
  - Information Gain Calculation :Let Attr be the set of all attributes and Ex the set of all training examples,value withx\in Ex defines the value of a specific example x for attribute a\in Attr, H specifies the entropy....
  
  
• Information ratio
  Information ratio
  The Information ratio is a measure of the risk-adjusted return of a financial security . It is also known as Appraisal ratio and is defined as expected active return divided by tracking error, where active return is the difference between the return of the security and the return of a selected...
  
   – finance
• Information source (mathematics)
  Information source (mathematics)
  In mathematics, an information source is a sequence of random variables ranging over a finite alphabet Γ, having a stationary distribution.The uncertainty, or entropy rate, of an information source is defined as...
  
  
• Information theory
  Information theory
  Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and...
  
  
• Inherent bias
  Inherent bias
  The term "inherent bias" refers to the effect of underlying factors or assumptions that skew viewpoints a subject under discussion. There are multiple formal definitions of "inherent bias" which depend on the particular field of study....
  
  
• Inherent zero
  Inherent zero
  In statistics, an inherent zero is a reference point used to describe data sets which are indicative of magnitude of an absolute or relative nature. Inherent zeros are used on ratio scales....
  
  
• Injury prevention
  Injury prevention
  Injury prevention are efforts to prevent or reduce the severity of bodily injuries caused by external mechanisms, such as accidents, before they occur. Injury prevention is a component of safety and public health, and its goal is to improve the health of the population by preventing injuries and...
  
   – application
• Innovation (signal processing)
  Innovation (signal processing)
  In time series analysis — as conducted in statistics, signal processing, and many other fields — the innovation is the difference between the observed value of a variable at time t and the optimal forecast of that value based on information available prior to time t...
  
  
• Innovations vector
  Innovations vector
  The innovations vector or residual vector is the difference between the measurement vector and the predicted measurement vector. Each difference represents the deviation of the observed random variable from the predicted response. The innovation vector is often used to check the validity of a...
  
  
• Institutional review board
  Institutional review board
  An institutional review board , also known as an independent ethics committee or ethical review board , is a committee that has been formally designated to approve, monitor, and review biomedical and behavioral research involving humans with the aim to protect the rights and welfare of the...
  
  
• Instrumental variable
  Instrumental variable
  In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables is used to estimate causal relationships when controlled experiments are not feasible....
  
  
• Intention to treat analysis
  Intention to treat analysis
  In epidemiology, an intention to treat analysis is an analysis based on the initial treatment intent, not on the treatment eventually administered. ITT analysis is intended to avoid various misleading artifacts that can arise in intervention research...
  
  
• Interaction (statistics)
  Interaction (statistics)
  In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive...
  
  
• Interaction variable – see Interaction (statistics)
  Interaction (statistics)
  In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive...
  
  
• Interclass correlation
  Interclass correlation
  In statistics, the interclass correlation measures a bivariate relation among variables.The Pearson correlation coefficient is the most commonly used interclass correlation....
  
  
• Interdecile range
  Interdecile range
  In statistics, the interdecile range is the difference between the first and the ninth deciles . The interdecile range is a measure of statistical dispersion of the values in a set of data, similar to the range and the interquartile range....
  
  
• Interim analysis
  Interim analysis
  Clinical trials are unique in that enrollment of patients is a continual process staggered in time. This means that if a treatment is particularly beneficial or harmful compared to the concurrent placebo group while the study is on-going, the investigators are ethically obliged to assess that...
  
  
• Internal consistency
  Internal consistency
  In statistics and research, internal consistency is typically a measure based on the correlations between different items on the same test . It measures whether several items that propose to measure the same general construct produce similar scores...
  
  
• Internal validity
  Internal validity
  Internal validity is the validity of inferences in scientific studies, usually based on experiments as experimental validity.- Details :...
  
  
• Interquartile mean
  Interquartile mean
  The interquartile mean is a statistical measure of central tendency, much like the mean , the median, and the mode....
  
  
• Interquartile range
  Interquartile range
  In descriptive statistics, the interquartile range , also called the midspread or middle fifty, is a measure of statistical dispersion, being equal to the difference between the upper and lower quartiles...
  
  
• Inter-rater reliability
  Inter-rater reliability
  In statistics, inter-rater reliability, inter-rater agreement, or concordance is the degree of agreement among raters. It gives a score of how much homogeneity, or consensus, there is in the ratings given by judges. It is useful in refining the tools given to human judges, for example by...
  
  
• Interval estimation
  Interval estimation
  In statistics, interval estimation is the use of sample data to calculate an interval of possible values of an unknown population parameter, in contrast to point estimation, which is a single number. Neyman identified interval estimation as distinct from point estimation...
  
  
• Intervening variable
  Intervening variable
  An intervening variable is a hypothetical internal state that is used to explain relationships between observed variables, such as independent and dependent variables, in empirical research.- History :...
  
  
• Intra-rater reliability
  Intra-rater reliability
  In statistics, intra-rater reliability is the degree of agreement among multiple repetitions of a diagnostic test performed by a single rater.-See also:* Inter-rater reliability* Reliability * Repeatability* Test-retest reliability...
  
  
• Intraclass correlation
  Intraclass correlation
  In statistics, the intraclass correlation is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other...
  
  
• Invariant estimator
  Invariant estimator
  In statistics, the concept of being an invariant estimator is a criterion that can be used to compare the properties of different estimators for the same quantity. It is a way of formalising the idea that an estimator should have certain intuitively appealing qualities...
  
  
• Invariant extended Kalman filter
  Invariant extended Kalman filter
  The invariant extended Kalman filter is a new version of the extended Kalman filter for nonlinear systems possessing symmetries . It combines the advantages of both the EKF and the recently introduced symmetry-preserving filters...
  
  
• Inverse distance weighting
  Inverse distance weighting
  Inverse distance weighting is a method for multivariate interpolation, a process of assigning values to unknown points by using values from usually scattered set of known points...
  
  
• Inverse Gaussian distribution
  Inverse Gaussian distribution
  | cdf = \Phi\left +\exp\left \Phi\left...
  
  
• Inverse Mills ratio
  Inverse Mills ratio
  In statistics, the inverse Mills ratio, named after John P. Mills, is the ratio of the probability density function to the cumulative distribution function of a distribution....
  
  
• Inverse probability
  Inverse probability
  In probability theory, inverse probability is an obsolete term for the probability distribution of an unobserved variable.Today, the problem of determining an unobserved variable is called inferential statistics, the method of inverse probability is called Bayesian probability, the "distribution"...
  
  
• Inverse relationship
  Inverse relationship
  An inverse or negative relationship is a mathematical relationship in which one variable, say y, decreases as another, say x, increases. For a linear relation, this can be expressed as y = a-bx, where -b is a constant value less than zero and a is a constant...
  
  
• Inverse-chi-squared distribution
• Inverse-gamma distribution
• Inverse transform sampling
• Inverse-variance weighting
  Inverse-variance weighting
  In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the sum. Each random variable in the sum is weighted in inverse proportion to its variance....
  
  
• Inverse-Wishart distribution
• Iris flower data set
  Iris flower data set
  The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Aylmer Fisher as an example of discriminant analysis...
  
  
• Irwin–Hall distribution
• Isomap
  Isomap
  In statistics, Isomap is one of several widely used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling . Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points...
  
  
• Isotonic regression
• Item response theory
  Item response theory
  In psychometrics, item response theory also known as latent trait theory, strong true score theory, or modern mental test theory, is a paradigm for the design, analysis, and scoring of tests, questionnaires, and similar instruments measuring abilities, attitudes, or other variables. It is based...
  
  
• Item-total correlation
  Item-total correlation
  The item-total correlation test arises in psychometrics in contexts where a number of tests or questions are given to an individual and where the problem is to construct a useful single quantity for each individual that can be used to compare that individual with others in a given population...
  
  
• Item tree analysis
  Item tree analysis
  Item tree analysis is a data analytical method which allows constructing ahierarchical structure on the items of a questionnaire or test from observed responsepatterns. Assume that we have a questionnaire with m items and that subjects can...
  
  
• Iterative proportional fitting
  Iterative proportional fitting
  The iterative proportional fitting procedure is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and the estimated table decomposes into an outer...
  
  
• Iteratively reweighted least squares
• Itō calculus
  Ito calculus
  Itō calculus, named after Kiyoshi Itō, extends the methods of calculus to stochastic processes such as Brownian motion . It has important applications in mathematical finance and stochastic differential equations....
  
  
• Itō isometry
  Ito isometry
  In mathematics, the Itō isometry, named after Kiyoshi Itō, is a crucial fact about Itō stochastic integrals. One of its main applications is to enable the computation of variances for stochastic processes....
  
  
• Itō's lemma
  Ito's lemma
  In mathematics, Itō's lemma is used in Itō stochastic calculus to find the differential of a function of a particular type of stochastic process. It is named after its discoverer, Kiyoshi Itō...
  
  

J

• Jaccard index
  Jaccard index
  The Jaccard index, also known as the Jaccard similarity coefficient , is a statistic used for comparing the similarity and diversity of sample sets....
  
  
• Jackknife (statistics) redirects to Resampling (statistics)
  Resampling (statistics)
  In statistics, resampling is any of a variety of methods for doing one of the following:# Estimating the precision of sample statistics by using subsets of available data or drawing randomly with replacement from a set of data points # Exchanging labels on data points when performing significance...
  
  
• Jackson network
• Jackson's theorem (queueing theory)
• Jadad scale
  Jadad scale
  The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to independently assess the methodological quality of a clinical trial...
  
  
• James–Stein estimator
• Jarque–Bera test
  Jarque–Bera test
  In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera...
  
  
• Jeffreys prior
  Jeffreys prior
  In Bayesian probability, the Jeffreys prior, named after Harold Jeffreys, is a non-informative prior distribution on parameter space that is proportional to the square root of the determinant of the Fisher information:...
  
  
• Jensen's inequality
  Jensen's inequality
  In mathematics, Jensen's inequality, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906. Given its generality, the inequality appears in many forms depending on the context,...
  
  
• Jensen–Shannon divergence
  Jensen–Shannon divergence
  In probability theory and statistics, the Jensen–Shannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius or total divergence to the average. It is based on the Kullback–Leibler divergence, with the notable ...
  
  
• JMulTi
  JMulTi
  JMulTi is an open-source interactive software for econometric analysis, specialised in univariate and multivariate time series analysis. It has a Java graphical user interface....
  
   – software
• Johansen test
  Johansen test
  In statistics, the Johansen test, named after Søren Johansen, is a procedure for testing cointegration of several I time series. This test permits more than one cointegrating relationship so is more generally applicable than the Engle–Granger test which is based on the Dickey–Fuller test for...
  
  
• Joint probability distribution
• JMP (statistical software)
  JMP (statistical software)
  JMP is a computer program that was first developed by John Sall and others to perform simple and complex statistical analyses.It dynamically links statistics with graphics to interactively explore, understand, and visualize data...
  
  
• Jump process
  Jump process
  A jump process is a type of stochastic process that has discrete movements, called jumps, rather than small continuous movements.In physics, jump processes result in diffusion...
  
  
• Jump-diffusion model
• Junction tree algorithm

K

• K-distribution
  K-distribution
  The K-distribution is a probability distribution that arises as the consequence of a statistical or probabilistic model used in Synthetic Aperture Radar imagery...
  
  
• K-means algorithm
  K-means algorithm
  In statistics and data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean...
  
   redirects to k-means clustering
• K-means++
  K-means++
  In applied statistics, k-means++ is an algorithm for choosing the initial values for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor...
  
  
• K-medians clustering
  K-medians clustering
  In statistics and machine learning, k-medians clustering is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median...
  
  
• K-medoids
  K-medoids
  The -medoids algorithm is a clustering algorithm related to the -means algorithm and the medoidshift algorithm. Both the -means and -medoids algorithms are partitional and both attempt to minimize squared error, the distance between points labeled to be in a cluster and a point designated as the...
  
  
• Kalman filter
  Kalman filter
  In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kálmán. Its purpose is to use measurements observed over time, containing noise and other inaccuracies, and produce values that tend to be closer to the true values of the measurements and their associated calculated...
  
  
• Kaplan–Meier estimator
• Kappa coefficient
• Kappa statistic
• Karhunen–Loève theorem
• Kendall tau distance
  Kendall tau distance
  The Kendall tau distance is a metric that counts the number of pairwise disagreements between two lists. The larger the distance, the more dissimilar the two lists are. Kendall tau distance is also called bubble-sort distance since it is equivalent to the number of swaps that the bubble sort...
  
  
• Kendall tau rank correlation coefficient
  Kendall tau rank correlation coefficient
  In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient, is a statistic used to measure the association between two measured quantities...
  
  
• Kendall's notation
  Kendall's notation
  In queueing theory, Kendall's notation is the standard system used to describe and classify the queueing model that a queueing system corresponds to. First suggested by D. G...
  
  
• Kendall's W
  Kendall's W
  Kendall's W is a non-parametric statistic. It is a normalization of the statistic of the Friedman test, and can be used for assessing agreement among raters...
  
   – Kendall's coefficient of concordance
• Kent distribution
• Kernel density estimation
  Kernel density estimation
  In statistics, kernel density estimation is a non-parametric way of estimating the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample...
  
  
• Kernel methods
  Kernel methods
  In computer science, kernel methods are a class of algorithms for pattern analysis, whose best known elementis the support vector machine...
  
  
• Kernel principal component analysis
• Kernel regression
  Kernel regression
  The kernel regression is a non-parametric technique in statistics to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y....
  
  
• Kernel smoother
  Kernel smoother
  A kernel smoother is a statistical technique for estimating a real valued function f\,\,\left by using its noisy observations, when no parametric model for this function is known...
  
  
• Kernel (statistics)
  Kernel (statistics)
  A kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables' density functions, or in kernel regression to estimate the conditional expectation of a random variable. Kernels are also used in time-series,...
  
  
• Khmaladze transformation
  Khmaladze transformation
  The Khmaladze Transformation is a statistical tool.Consider the sequence of empirical distribution functions F_n based on asequence of i.i.d random variables, X_1,\ldots, X_n, as n increases.Suppose F is the hypothetical distribution function of...
  
   (probability theory)
• Killed process
  Killed process
  In probability theory — specifically, in stochastic analysis — a killed process is a stochastic process that is forced to assume an undefined or "killed" state at some time.-Definition:...
  
  
• Khintchine inequality
  Khintchine inequality
  In mathematics, the Khintchine inequality, named after Aleksandr Khinchin and spelled in multiple ways in the Roman alphabet, is a theorem from probability, and is also frequently used in analysis...
  
  
• Kingman's formula
• Kirkwood approximation
  Kirkwood approximation
  The Kirkwood superposition approximation was introduced by Matsuda as a means of representing a discrete probability distribution. The name apparently refers to a 1942 paper by John G. Kirkwood...
  
  
• Kish grid
  Kish grid
  The Kish grid is a method for selecting members within a household to be interviewed. In telephone surveys, the next-birthday method is sometimes preferred to the Kish grid.- References :...
  
  
• Kitchen sink regression
  Kitchen sink regression
  A kitchen sink regression is an informal and usually pejorative term for a regression analysis which uses a long list of possible independent variables to attempt to explain variance in a dependent variable. In economics, psychology, and other social sciences, regression analysis is typically used...
  
  
• Knightian uncertainty
  Knightian uncertainty
  In economics, Knightian uncertainty is risk that is immeasurable, not possible to calculate.Knightian uncertainty is named after University of Chicago economist Frank Knight , who distinguished risk and uncertainty in his work Risk, Uncertainty, and Profit:- Common-cause and special-cause :The...
  
  
• Kolmogorov backward equation
  Kolmogorov backward equation
  The Kolmogorov backward equation and its adjoint sometimes known as the Kolmogorov forward equation are partial differential equations that arise in the theory of continuous-time continuous-state Markov processes. Both were published by Andrey Kolmogorov in 1931...
  
  
• Kolmogorov continuity theorem
  Kolmogorov continuity theorem
  In mathematics, the Kolmogorov continuity theorem is a theorem that guarantees that a stochastic process that satisfies certain constraints on the moments of its increments will be continuous...
  
  
• Kolmogorov extension theorem
  Kolmogorov extension theorem
  In mathematics, the Kolmogorov extension theorem is a theorem that guarantees that a suitably "consistent" collection of finite-dimensional distributions will define a stochastic process...
  
  
• Kolmogorov’s criterion
  Kolmogorov’s criterion
  In probability theory, Kolmogorov's criterion, named after Andrey Kolmogorov, is a theorem in Markov processes concerning stationary Markov chains...
  
  
• Kolmogorov’s generalized criterion
• Kolmogorov's inequality
  Kolmogorov's inequality
  In probability theory, Kolmogorov's inequality is a so-called "maximal inequality" that gives a bound on the probability that the partial sums of a finite collection of independent random variables exceed some specified bound...
  
  
• Kolmogorov's zero-one law
  Kolmogorov's zero-one law
  In probability theory, Kolmogorov's zero-one law, named in honor of Andrey Nikolaevich Kolmogorov, specifies that a certain type of event, called a tail event, will either almost surely happen or almost surely not happen; that is, the probability of such an event occurring is zero or one.Tail...
  
  
• Kolmogorov–Smirnov test
• KPSS test
  KPSS test
  In econometrics, Kwiatkowski–Phillips–Schmidt–Shin tests are used for testing a null hypothesis that an observable time series is stationary around a deterministic trend. Such models were proposed in 1982 by Alok Bhargava in his Ph.D. thesis where several John von Neumann or Durbin–Watson type...
  
  
• Kriging
  Kriging
  Kriging is a group of geostatistical techniques to interpolate the value of a random field at an unobserved location from observations of its value at nearby locations....
  
  
• Kruskal–Wallis one-way analysis of variance
• Kuder-Richardson Formula 20
  Kuder-Richardson Formula 20
  In statistics, the Kuder-Richardson Formula 20 first published in 1937 is a measure of internal consistency reliability for measures with dichotomous choices. It is analogous to Cronbach's α, except Cronbach's α is also used for non-dichotomous measures...
  
  
• Kuiper's test
  Kuiper's test
  Kuiper's test is used in statistics to test that whether a given distribution, or family of distributions, is contradicted by evidence from a sample of data. It is named after Dutch mathematician Nicolaas Kuiper....
  
  
• Kullback's inequality
  Kullback's inequality
  In information theory and statistics, Kullback's inequality is a lower bound on the Kullback–Leibler divergence expressed in terms of the large deviations rate function. If P and Q are probability distributions on the real line, such that P is absolutely continuous with respect to Q, i.e...
  
  
• Kullback–Leibler divergence
  Kullback–Leibler divergence
  In probability theory and information theory, the Kullback–Leibler divergence is a non-symmetric measure of the difference between two probability distributions P and Q...
  
  
• Kumaraswamy distribution
  Kumaraswamy distribution
  In probability and statistics, the Kumaraswamy's double bounded distribution is a family of continuous probability distributions defined on the interval [0,1] differing in the values of their two non-negative shape parameters, a and b....
  
  
• Kurtosis
  Kurtosis
  In probability theory and statistics, kurtosis is any measure of the "peakedness" of the probability distribution of a real-valued random variable...
  
  
• Kushner equation
  Kushner equation
  In filtering theory the Kushner equation is an equation for the conditional probability density of the state of a stochastic non-linear dynamical system, given noisy measurements of the state. It therefore provides the solution of the nonlinear filtering problem in estimation theory...
  
  

L

• L-estimator
• L-moment
  L-moment
  In statistics, L-moments are statistics used to summarize the shape of a probability distribution. They are analogous to conventional moments in that they can be used to calculate quantities analogous to standard deviation, skewness and kurtosis, termed the L-scale, L-skewness and L-kurtosis...
  
  
• Labour Force Survey
  Labour Force Survey
  Labour Force Surveys are statistical surveys conducted in a number of countries designed to capture data about the labour market. All European Union member states are required to conduct a Labour Force Survey annually. Labour Force Surveys are also carried out in some non-EU countries. They are...
  
  
• Lack-of-fit sum of squares
  Lack-of-fit sum of squares
  In statistics, a sum of squares due to lack of fit, or more tersely a lack-of-fit sum of squares, is one of the components of a partition of the sum of squares in an analysis of variance, used in the numerator in an F-test of the null hypothesis that says that a proposed model fits well.- Sketch of...
  
  
• Lady tasting tea
  Lady tasting tea
  In the design of experiments in statistics, the lady tasting tea is a famous randomized experiment devised by Ronald A. Fisher and reported in his book Statistical methods for research workers . The lady in question was Dr...
  
  
• Lag operator
• Lag windowing
  Lag windowing
  Lag windowing is a technique that consists of windowing the auto-correlation coefficients prior to estimating Linear prediction coefficients . The windowing in the auto-correlation domain has the same effect as a convolution in the power spectral domain and helps stabilizing the result of the...
  
  
• Lambda distribution — disambiguation
• Landau distribution
• Lander–Green algorithm
  Lander–Green algorithm
  The Lander–Green algorithm is an algorithm, due to Eric Lander and Philip Green for computing the likelihood of observed genotype data given a pedigree. It is appropriate for relatively small pedigrees and a large number of markers. It is used in the analysis of genetic linkage....
  
  
• Language model
  Language model
  A statistical language model assigns a probability to a sequence of m words P by means of a probability distribution.Language modeling is used in many natural language processing applications such as speech recognition, machine translation, part-of-speech tagging, parsing and information...
  
  
• Laplace distribution
• Laplace principle (large deviations theory)
  Laplace principle (large deviations theory)
  In mathematics, Laplace's principle is a basic theorem in large deviations theory, similar to Varadhan's lemma. It gives an asymptotic expression for the Lebesgue integral of exp over a fixed set A as θ becomes large...
  
  
• Large deviations theory
  Large deviations theory
  In probability theory, the theory of large deviations concerns the asymptotic behaviour of remote tails of sequences of probability distributions. Some basic ideas of the theory can be tracked back to Laplace and Cramér, although a clear unified formal definition was introduced in 1966 by Varadhan...
  
  
• Large deviations of Gaussian random functions
  Large deviations of Gaussian random functions
  A random function – of either one variable , or two or more variables – is called Gaussian if every finite-dimensional distribution is a multivariate normal distribution. Gaussian random fields on the sphere are useful when analysing* the anomalies in the cosmic microwave background...
  
  
• LARS — see least-angle regression
  Least-angle regression
  In statistics, least-angle regression is a regression algorithm for high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani....
  
  
• Latent variable
  Latent variable
  In statistics, latent variables , are variables that are not directly observed but are rather inferred from other variables that are observed . Mathematical models that aim to explain observed variables in terms of latent variables are called latent variable models...
  
  , latent variable model
  Latent variable model
  A latent variable model is a statistical model that relates a set of variables to a set of latent variables.It is assumed that 1) the responses on the indicators or manifest variables are the result of...
  
  
• Latent class model
  Latent class model
  In statistics, a latent class model relates a set of observed discrete multivariate variables to a set of latent variables. It is a type of latent variable model. It is called a latent class model because the latent variable is discrete...
  
  
• Latent Dirichlet allocation
  Latent Dirichlet allocation
  In statistics, latent Dirichlet allocation is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar...
  
  
• Latent growth modeling
  Latent growth modeling
  Latent growth modeling is a statistical technique used in the structural equation modeling framework to estimate growth trajectory. It is a longitudinal analysis technique to estimate growth over a period of time. It is widely used in the field of behavioral science, education and social science. ...
  
  
• Latent semantic analysis
  Latent semantic analysis
  Latent semantic analysis is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close...
  
  
• Latin rectangle
  Latin rectangle
  In combinatorial mathematics, a Latin rectangle is an r × n matrix that has the numbers 1, 2, 3, ..., n as its entries with no number occurring more than once in any row or column where r ≤ n. An n × n Latin rectangle is called a...
  
  
• Latin square
  Latin square
  In combinatorics and in experimental design, a Latin square is an n × n array filled with n different symbols, each occurring exactly once in each row and exactly once in each column...
  
  
• Latin hypercube sampling
  Latin hypercube sampling
  Latin hypercube sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution. The sampling method is often applied in uncertainty analysis....
  
  
• Law (stochastic processes)
  Law (stochastic processes)
  In mathematics, the law of a stochastic process is the measure that the process induces on the collection of functions from the index set into the state space...
  
  
• Law of averages
  Law of averages
  The law of averages is a lay term used to express a belief that outcomes of a random event will "even out" within a small sample.As invoked in everyday life, the "law" usually reflects bad statistics or wishful thinking rather than any mathematical principle...
  
  
• Law of comparative judgment
  Law of comparative judgment
  The law of comparative judgment was conceived by L. L. Thurstone. In modern day terminology, it is more aptly described as a model that is used to obtain measurements from any process of pairwise comparison...
  
  
• Law of large numbers
  Law of large numbers
  In probability theory, the law of large numbers is a theorem that describes the result of performing the same experiment a large number of times...
  
  
• Law of the iterated logarithm
  Law of the iterated logarithm
  In probability theory, the law of the iterated logarithm describes the magnitude of the fluctuations of a random walk. The original statement of the law of the iterated logarithm is due to A. Y. Khinchin . Another statement was given by A.N...
  
  
• Law of the unconscious statistician
  Law of the unconscious statistician
  In probability theory and statistics, the law of the unconscious statistician is a theorem used to calculate the expected value of a function g of a random variable X when one knows the probability distribution of X but one does not explicitly know the distribution of g.The form of the law can...
  
  
• Law of total covariance
  Law of total covariance
  In probability theory, the law of total covariance or covariance decomposition formula states that if X, Y, and Z are random variables on the same probability space, and the covariance of X and Y is finite, then...
  
  
• Law of total cumulance
  Law of total cumulance
  In probability theory and mathematical statistics, the law of total cumulance is a generalization to cumulants of the law of total probability, the law of total expectation, and the law of total variance. It has applications in the analysis of time series...
  
  
• Law of total expectation
  Law of total expectation
  The proposition in probability theory known as the law of total expectation, the law of iterated expectations, the tower rule, the smoothing theorem, among other names, states that if X is an integrable random variable The proposition in probability theory known as the law of total expectation, ...
  
  
• Law of total probability
  Law of total probability
  In probability theory, the law of total probability is a fundamental rule relating marginal probabilities to conditional probabilities.-Statement:The law of total probability is the proposition that if \left\...
  
  
• Law of total variance
  Law of total variance
  In probability theory, the law of total variance or variance decomposition formula states that if X and Y are random variables on the same probability space, and the variance of Y is finite, then...
  
  
• Law of Truly Large Numbers
  Law of Truly Large Numbers
  The law of truly large numbers, attributed to Persi Diaconis and Frederick Mosteller, states that with a sample size large enough, any outrageous thing is likely to happen. Because we never find it notable when likely events occur, we highlight unlikely events and notice them more...
  
  
• Layered hidden Markov model
  Layered hidden Markov model
  The layered hidden Markov model is a statistical model derived from the hidden Markov model .A layered hidden Markov model consists of N levels of HMMs, where the HMMs on level i + 1 correspond to observation symbols or probability generators at level i.Every level i of the LHMM...
  
  
• Le Cam's theorem
  Le Cam's theorem
  In probability theory, Le Cam's theorem, named after Lucien le Cam , is as follows.Suppose:* X1, ..., Xn are independent random variables, each with a Bernoulli distribution , not necessarily identically distributed.* Pr = pi for i = 1, 2, 3, ...* \lambda_n = p_1 + \cdots + p_n.\,* S_n = X_1...
  
  
• Lead time bias
  Lead time bias
  Lead time is the length of time between the detection of a disease and its usual clinical presentation and diagnosis ....
  
  
• Least absolute deviations
  Least absolute deviations
  Least absolute deviations , also known as Least Absolute Errors , Least Absolute Value , or the L1 norm problem, is a mathematical optimization technique similar to the popular least squares technique that attempts to find a function which closely approximates a set of data...
  
  
• Least-angle regression
  Least-angle regression
  In statistics, least-angle regression is a regression algorithm for high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani....
  
  
• Least squares
  Least squares
  The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. "Least squares" means that the overall solution minimizes the sum of the squares of the errors made in solving every...
  
  
• Least-squares spectral analysis
  Least-squares spectral analysis
  Least-squares spectral analysis is a method of estimating a frequency spectrum, based on a least squares fit of sinusoids to data samples, similar to Fourier analysis...
  
  
• Least squares support vector machine
  Least squares support vector machine
  Least squares support vector machines are least squares versions of support vector machines , which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis...
  
  
• Least trimmed squares
  Least Trimmed Squares
  Least trimmed squares , or least trimmed sum of squares, is a robust statistical method that attempts to fit a function to a set of data whilst not being unduly affected by the presence of outliers...
  
  
• Learning theory (statistics)
• Leftover hash-lemma
  Leftover hash-lemma
  The leftover hash lemma is a lemma in cryptography first stated by Russell Impagliazzo, Leonid Levin, and Michael Luby.Imagine that you have a secret key X that has n uniform random bits, and you would like to use this secret key to encrypt a message. Unfortunately, you were a bit careless with the...
  
  
• Lehmann–Scheffé theorem
  Lehmann–Scheffé theorem
  In statistics, the Lehmann–Scheffé theorem is prominent in mathematical statistics, tying together the ideas of completeness, sufficiency, uniqueness, and best unbiased estimation...
  
  
• Length time bias
  Length time bias
  Length time bias is a form of selection bias, a statistical distortion of results which can lead to incorrect conclusions about the data. Length time bias can occur when the lengths of intervals are analysed by selecting intervals that occupy randomly chosen points in time or space...
  
  
• Levene's test
  Levene's test
  In statistics, Levene's test is an inferential statistic used to assess the equality of variances in different samples. Some common statistical procedures assume that variances of the populations from which different samples are drawn are equal. Levene's test assesses this assumption. It tests the...
  
  
• Level of measurement
  Level of measurement
  The "levels of measurement", or scales of measure are expressions that typically refer to the theory of scale types developed by the psychologist Stanley Smith Stevens. Stevens proposed his theory in a 1946 Science article titled "On the theory of scales of measurement"...
  
  
• Levenberg–Marquardt algorithm
• Leverage (statistics)
  Leverage (statistics)
  In statistics, leverage is a term used in connection with regression analysis and, in particular, in analyses aimed at identifying those observations that are far away from corresponding average predictor values...
  
  
• Levey–Jennings chart — redirects to Laboratory quality control
  Laboratory quality control
  Laboratory quality control is designed to detect, reduce, and correct deficiencies in a laboratory's internal analytical process prior to the release of patient results and improve the quality of the results reported by the laboratory. Quality control is a measure of precision or how well the...
  
  
• Lévy's convergence theorem
• Lévy's continuity theorem
  Lévy's continuity theorem
  In probability theory, the Lévy’s continuity theorem, named after the French mathematician Paul Lévy, connects convergence in distribution of the sequence of random variables with pointwise convergence of their characteristic functions...
  
  
• Lévy arcsine law
  Lévy arcsine law
  In probability theory, the Lévy arcsine law, found by , states that the probability distribution of the proportion of the time that a Wiener process is positive is a random variable whose probability distribution is the arcsine distribution...
  
  
• Lévy distribution
• Lévy flight
  Lévy flight
  A Lévy flight is a random walk in which the step-lengths have a probability distribution that is heavy-tailed. When defined as a walk in a space of dimension greater than one, the steps made are in isotropic random directions...
  
  
• Lévy process
  Lévy process
  In probability theory, a Lévy process, named after the French mathematician Paul Lévy, is any continuous-time stochastic process that starts at 0, admits càdlàg modification and has "stationary independent increments" — this phrase will be explained below...
  
  
• Lewontin's Fallacy
  Lewontin's Fallacy
  Human genetic diversity: Lewontin's fallacy is a 2003 paper by A. W. F. Edwards that refers to an argument first made by Richard Lewontin in his 1972 article The apportionment of human diversity, which argued that race for humans is not a valid taxonomic construct. Edwards' paper criticized and...
  
  
• Lexis diagram
  Lexis diagram
  In demography a Lexis diagram is a two dimensional diagram that is used to represent events that occur to individuals belonging to different cohorts...
  
  
• Lexis ratio
  Lexis ratio
  The Lexis ratio is used in statistics as a measure which seeks to evaluate differences between the statistical properties of random mechanisms where the outcome is two-valued — for example "success" or "failure", "win" or "lose"...
  
  
• Lies, damned lies, and statistics
  Lies, damned lies, and statistics
  "Lies, damned lies, and statistics" is a phrase describing the persuasive power of numbers, particularly the use of statistics to bolster weak arguments...
  
  
• Life expectancy
  Life expectancy
  Life expectancy is the expected number of years of life remaining at a given age. It is denoted by ex, which means the average number of subsequent years of life for someone now aged x, according to a particular mortality experience...
  
  
• Life table
  Life table
  In actuarial science, a life table is a table which shows, for each age, what the probability is that a person of that age will die before his or her next birthday...
  
  
• Lift (data mining)
  Lift (data mining)
  In data mining, lift is a measure of the performance of a model at predicting or classifying cases, measuring against a random choice model.For example, suppose a population has a predicted response rate of 5%, but a certain model has identified a segment with a predicted response rate of 20%...
  
  
• Likelihood function
  Likelihood function
  In statistics, a likelihood function is a function of the parameters of a statistical model, defined as follows: the likelihood of a set of parameter values given some observed outcomes is equal to the probability of those observed outcomes given those parameter values...
  
  
• Likelihood principle
  Likelihood principle
  In statistics,the likelihood principle is a controversial principle of statistical inference which asserts that all of the information in a sample is contained in the likelihood function....
  
  
• Likelihood-ratio test
  Likelihood-ratio test
  In statistics, a likelihood ratio test is a statistical test used to compare the fit of two models, one of which is a special case of the other . The test is based on the likelihood ratio, which expresses how many times more likely the data are under one model than the other...
  
  
• Likelihood ratios in diagnostic testing
  Likelihood ratios in diagnostic testing
  In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition exists.-Calculation:Two versions of the...
  
  
• Likert scale
  Likert scale
  A Likert scale is a psychometric scale commonly involved in research that employs questionnaires. It is the most widely used approach to scaling responses in survey research, such that the term is often used interchangeably with rating scale, or more accurately the Likert-type scale, even though...
  
  
• Lilliefors test
  Lilliefors test
  In statistics, the Lilliefors test, named after Hubert Lilliefors, professor of statistics at George Washington University, is an adaptation of the Kolmogorov–Smirnov test...
  
  
• Limited dependent variable
  Limited dependent variable
  A limited dependent variable is a variable whose range ofpossible values is "restricted in some important way." In econometrics, the term is often used whenestimation of the relationship between the limited dependent variable...
  
  
• Limiting density of discrete points
  Limiting density of discrete points
  In information theory, the limiting density of discrete points is an adjustment to the formula of Claude Elwood Shannon for differential entropy.It was formulated by Edwin Thompson Jaynes to address defects in the initial definition of differential entropy....
  
  
• Lincoln index
  Lincoln Index
  The Lincoln index is a statistical measure used in several fields to estimate the number of cases that have not yet been observed, based on two independent sets of observed cases. It is also sometimes known as the Lincoln-Petersen method.-Applications:...
  
  
• Lindeberg's condition
  Lindeberg's condition
  In probability theory, Lindeberg's condition is a sufficient condition for the central limit theorem to hold for a sequence of independent random variables...
  
  
• Lindley equation
  Lindley equation
  In probability theory, the Lindley equation, Lindley recursion or Lindley processes is a discrete time stochastic process An where n takes integer values and...
  
  
• Lindley's paradox
  Lindley's paradox
  Lindley's paradox is a counterintuitive situation in statistics in which the Bayesian and frequentist approaches to a hypothesis testing problem give opposite results for certain choices of the prior distribution...
  
  
• Line chart
  Line chart
  A line chart or line graph is a type of graph, which displays information as a series of data points connected by straight line segments. It is a basic type of chart common in many fields. It is an extension of a scatter graph, and is created by connecting a series of points that represent...
  
  
• Line-intercept sampling
  Line-intercept sampling
  In statistics, line-intercept sampling is a method of sampling elements in a region whereby an element is sampled if a chosen line segment, called a “transect”, intersects the element ....
  
  
• Linear classifier
  Linear classifier
  In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics...
  
  
• Linear discriminant analysis
  Linear discriminant analysis
  Linear discriminant analysis and the related Fisher's linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events...
  
  
• Linear least squares — disambiguation
• Linear least squares (mathematics)
• Linear model
  Linear model
  In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However the term is also used in time series analysis with a different...
  
  
• Linear prediction
  Linear prediction
  Linear prediction is a mathematical operation where future values of a discrete-time signal are estimated as a linear function of previous samples....
  
  
• Linear probability model
• Linear regression
  Linear regression
  In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple regression...
  
  
• Linguistic demography
• LISREL
  LISREL
  LISREL, an acronym for linear structural relations, is a statistical software package used in structural equation modeling. LISREL was developed in 1970s by Karl Jöreskog, then a scientist at Educational Testing Service in Princeton, NJ, and Dag Sörbom, later both professors of Uppsala University,...
  
   — proprietary statistical software package
• List of basic statistics topics — redirects to Outline of statistics
• List of convolutions of probability distributions
• List of graphical methods
• List of information graphics software
• List of probability topics
• List of random number generators
• List of scientific journals in statistics
• List of statistical packages
• List of statisticians
• Listwise deletion
• Little's law
  Little's law
  In the mathematical theory of queues, Little's result, theorem, lemma, law or formula says:It is a restatement of the Erlang formula, based on the work of Danish mathematician Agner Krarup Erlang...
  
  
• Littlewood's law
  Littlewood's law
  Littlewood's Law states that individuals can expect a "miracle" to happen to them at the rate of about one per month.-History:The law was framed by Cambridge University Professor J. E...
  
  
• Ljung–Box test
  Ljung–Box test
  The Ljung–Box test is a type of statistical test of whether any of a group of autocorrelations of a time series are different from zero...
  
  
• Local convex hull
• Local independence
  Local independence
  Local independence is the underlying assumption of latent variable models.The observed items are conditionally independent of each other given an individual score on the latent variable. This means that the latent variable explains why the observed items are related to another...
  
  
• Local martingale
  Local martingale
  In mathematics, a local martingale is a type of stochastic process, satisfying the localized version of the martingale property. Every martingale is a local martingale; every bounded local martingale is a martingale; however, in general a local martingale is not a martingale, because its...
  
  
• Local regression
  Local regression
  LOESS, or LOWESS , is one of many "modern" modeling methods that build on "classical" methods, such as linear and nonlinear least squares regression. Modern regression methods are designed to address situations in which the classical procedures do not perform well or cannot be effectively applied...
  
  
• Location estimation redirects to Location parameter
  Location parameter
  In statistics, a location family is a class of probability distributions that is parametrized by a scalar- or vector-valued parameter μ, which determines the "location" or shift of the distribution...
  
  
• Location estimation in sensor networks
  Location estimation in sensor networks
  Location estimation in wireless sensor networks is the problem of estimating the location of an object from a set of noisy measurements, when the measurements are acquired in a distributedmanner by a set of sensors.-Motivation:...
  
  
• Location parameter
  Location parameter
  In statistics, a location family is a class of probability distributions that is parametrized by a scalar- or vector-valued parameter μ, which determines the "location" or shift of the distribution...
  
  
• Location test
  Location test
  A location test is a statistical hypothesis test that compares the location parameter of a statistical population to a given constant, or that compares the location parameters of two statistical populations to each other...
  
  
• Location-scale family
  Location-scale family
  In probability theory, especially as that field is used in statistics, a location-scale family is a family of univariate probability distributions parametrized by a location parameter and a non-negative scale parameter; if X is any random variable whose probability distribution belongs to such a...
  
  
• Local asymptotic normality
  Local asymptotic normality
  In statistics, local asymptotic normality is a property of a sequence of statistical models, which allows this sequence to be asymptotically approximated by a normal location model, after a rescaling of the parameter...
  
  
• Locality (statistics)
• Loess curve redirects to Local regression
  Local regression
  LOESS, or LOWESS , is one of many "modern" modeling methods that build on "classical" methods, such as linear and nonlinear least squares regression. Modern regression methods are designed to address situations in which the classical procedures do not perform well or cannot be effectively applied...
  
  
• Log-Cauchy distribution
  Log-Cauchy distribution
  In probability theory, a log-Cauchy distribution is a probability distribution of a random variable whose logarithm is distributed in accordance with a Cauchy distribution...
  
  
• Log-Laplace distribution
  Log-Laplace distribution
  In probability theory and statistics, the log-Laplace distribution is the probability distribution of a random variable whose logarithm has a Laplace distribution. If X has a Laplace distribution with parameters μ and b, then Y = eX has a log-Laplace distribution...
  
  
• Log-normal distribution
• Log-linear model
• Log-linear modeling
• Log-log graph
  Log-log graph
  In science and engineering, a log-log graph or log-log plot is a two-dimensional graph of numerical data that uses logarithmic scales on both the horizontal and vertical axes...
  
  
• Log-logistic distribution
  Log-logistic distribution
  In probability and statistics, the log-logistic distribution is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for events whose rate increases initially and decreases later, for example mortality from cancer following...
  
  
• Logarithmic distribution
• Logarithmic mean
  Logarithmic mean
  In mathematics, the logarithmic mean is a function of two non-negative numbers which is equal to their difference divided by the logarithm of their quotient...
  
  
• Logistic distribution
• Logistic function
  Logistic function
  A logistic function or logistic curve is a common sigmoid curve, given its name in 1844 or 1845 by Pierre François Verhulst who studied it in relation to population growth. It can model the "S-shaped" curve of growth of some population P...
  
  
• Logistic regression
  Logistic regression
  In statistics, logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression...
  
  
• Logit
  Logit
  The logit function is the inverse of the sigmoidal "logistic" function used in mathematics, especially in statistics.Log-odds and logit are synonyms.-Definition:The logit of a number p between 0 and 1 is given by the formula:...
  
  
• Logit analysis in marketing
  Logit analysis in marketing
  Logit analysis is a statistical technique used by marketers to assess the scope of customer acceptance of a product, particularly a new product. It attempts to determine the intensity or magnitude of customers' purchase intentions and translates that into a measure of actual buying behaviour...
  
  
• Logit-normal distribution
• Lognormal distribution
• Logrank test
  Logrank test
  In statistics, the logrank test is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right skewed and censored...
  
  
• Lomax distribution
• Long-range dependency
  Long-range dependency
  Long-range dependency is a phenomenon that may arise in the analysis of spatial or time series data. It relates to the rate of decay of statistical dependence, with the implication that this decays more slowly than an exponential decay, typically a power-like decay...
  
  
• Long Tail
  Long tail
  Long tail may refer to:*The Long Tail, a consumer demographic in business*Power law's long tail, a statistics term describing certain kinds of distribution*Long-tail boat, a type of watercraft native to Southeast Asia...
  
  
• Long-tail traffic
  Long-tail traffic
  This article covers a range of tools from different disciplines that may be used in the important science of determining the probability of rare events....
  
  
• Longitudinal study
  Longitudinal study
  A longitudinal study is a correlational research study that involves repeated observations of the same variables over long periods of time — often many decades. It is a type of observational study. Longitudinal studies are often used in psychology to study developmental trends across the...
  
  
• Lorenz curve
  Lorenz curve
  In economics, the Lorenz curve is a graphical representation of the cumulative distribution function of the empirical probability distribution of wealth; it is a graph showing the proportion of the distribution assumed by the bottom y% of the values...
  
  
• Loss function
  Loss function
  In statistics and decision theory a loss function is a function that maps an event onto a real number intuitively representing some "cost" associated with the event. Typically it is used for parameter estimation, and the event in question is some function of the difference between estimated and...
  
  
• Lot quality assurance sampling
  Lot Quality Assurance Sampling
  Lot quality assurance sampling is a simple, low-cost random sampling methodology developed in the 1920s to control the quality of output in industrial production processes....
  
  
• Lotka's law
• Low birth weight paradox
  Low birth weight paradox
  The low birth weight paradox is an apparently paradoxical observation relating to the birth weights and mortality of children born to tobacco smoking mothers. Low birth weight children born to smoking mothers have a lower infant mortality rate than the low birth weight children of non-smokers...
  
  
• Lucia de Berk
  Lucia de Berk
  Lucia de Berk, often called Lucia de B. or Lucy de B is a Dutch licenced paediatric nurse, who was subject to a miscarriage of justice. She was sentenced to life imprisonment in 2003 for four murders and three attempted murders of patients in her care...
  
   – prob/stats related court case
• Lukacs's proportion-sum independence theorem
  Lukacs's proportion-sum independence theorem
  In statistics, Lukacs's proportion-sum independence theorem is a result that is used when studying proportions, in particular the Dirichlet distribution...
  
  
• Lumpability
  Lumpability
  In probability theory, lumpability is a method for reducing the size of the state space of some continuous-time Markov chains, first published by Kemeny and Snell.-Definition:...
  
  
• Lusser's law
  Lusser's Law
  Lusser's law, named after Robert Lusser, is a prediction of reliability named after Robert Lusser. It is also called the "probability product law of series components". It states that the reliability of a series system is equal to the product of the reliability of its component subsystems, if their...
  
  
• Lyapunov's central limit theorem

M

• M/G/1 model
• M/M/1 model
  M/M/1 model
  In queueing theory, a discipline within the mathematical theory of probability, a M/M/1 queue represents the queue length in a system having a single server, where arrivals are detemined by a Poisson process and job service times have an exponential distribution. The model name is written in...
  
  
• M/M/c model
  M/M/c model
  In the mathematical theory of random processes, the M/M/c queue is a multi-server queue model. It is a generalisation of the M/M/1 queue.Following Kendall's notation it indicates a system where:*Arrivals are a Poisson process...
  
  
• M-estimator
  M-estimator
  In statistics, M-estimators are a broad class of estimators, which are obtained as the minima of sums of functions of the data. Least-squares estimators and many maximum-likelihood estimators are M-estimators. The definition of M-estimators was motivated by robust statistics, which contributed new...
  
  
  • Redescending M-estimator
    Redescending M-estimator
    In statistics, Redescending M-estimators are Ψ-type M-estimators which have Ψ functions that are non-decreasing near the origin, but decreasing toward 0 far from the origin...
    
    
• M-separation
  M-separation
  In statistics, m-separation is a measure of disconnectedness in ancestral graphs and a generalization of d-separation for directed acyclic graphs. It is the opposite of m-connectedness....
  
  
• Machine learning
  Machine learning
  Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
  
  
• Mahalanobis distance
  Mahalanobis distance
  In statistics, Mahalanobis distance is a distance measure introduced by P. C. Mahalanobis in 1936. It is based on correlations between variables by which different patterns can be identified and analyzed. It gauges similarity of an unknown sample set to a known one. It differs from Euclidean...
  
  
• Main effect
  Main effect
  In the design of experiments and analysis of variance, a main effect is the effect of an independent variable on a dependent variable averaging across the levels of any other independent variables...
  
  
• Mallows' Cp
  Mallows' Cp
  In statistics, Mallows' Cp, named for Colin L. Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares. It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the...
  
  
• MANCOVA
  MANCOVA
  Multivariate analysis of covariance is an extension of analysis of covariance methods to cover cases where there is more than one dependent variable and where the dependent variables cannot simply be combined....
  
  
• Manhattan plot
  Manhattan plot
  A Manhattan plot is a type of scatter plot, usually used to display data with a large number of data-points - many of non-zero amplitude, and with a distribution of higher-magnitude values, for instance in genome-wide association studies...
  
  
• Mann–Whitney U
• MANOVA
  MANOVA
  Multivariate analysis of variance is a generalized form of univariate analysis of variance . It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable have significant effects on the dependent variables; 2. what are the interactions...
  
  
• Mantel test
  Mantel test
  The Mantel test, named after Nathan Mantel, is a statistical test of the correlation between two matrices. The matrices must be of the same rank, in most applications they are matrices of interrelations between the same vectors of objects....
  
  
• MAP estimator — redirects to Maximum a posteriori estimation
• Marchenko–Pastur distribution
  Marchenko–Pastur distribution
  In random matrix theory, the Marchenko–Pastur distribution, or Marchenko–Pastur law, describes the asymptotic behavior of singular values of large rectangular random matrices...
  
  
• Marcinkiewicz–Zygmund inequality
  Marcinkiewicz–Zygmund inequality
  In mathematics, the Marcinkiewicz–Zygmund inequality, named after Józef Marcinkiewicz and Antoni Zygmund, gives relations between moments of a collection of independent random variables...
  
  
• Marcum Q-function
• Margin of error
  Margin of error
  The margin of error is a statistic expressing the amount of random sampling error in a survey's results. The larger the margin of error, the less faith one should have that the poll's reported results are close to the "true" figures; that is, the figures for the whole population...
  
  
• Marginal distribution
  Marginal distribution
  In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. The term marginal variable is used to refer to those variables in the subset of variables being retained...
  
  
• Marginal likelihood
  Marginal likelihood
  In statistics, a marginal likelihood function, or integrated likelihood, is a likelihood function in which some parameter variables have been marginalised...
  
  
• Marginal model
  Marginal model
  In statistics, marginal models are a technique for obtaining regression estimates in multilevel modeling, also called hierarchical linear models....
  
  
• Marginal variable — redirects to Marginal distribution
  Marginal distribution
  In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. The term marginal variable is used to refer to those variables in the subset of variables being retained...
  
  
• Mark and recapture
  Mark and recapture
  Mark and recapture is a method commonly used in ecology to estimate population size. This method is most valuable when a researcher fails to detect all individuals present within a population of interest every time that researcher visits the study area...
  
  
• Markov additive process
• Markov blanket
  Markov blanket
  In machine learning, the Markov blanket for a node A in a Bayesian network is the set of nodes \partial A composed of A's parents, its children, and its children's other parents. In a Markov network, the Markov blanket of a node is its set of neighbouring nodes...
  
  
• Markov chain
  Markov chain
  A Markov chain, named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memoryless: the next state depends only on the current state and not on the...
  
  
  • Markov chain geostatistics
    Markov chain geostatistics
    Markov chain geostatistics refer to the Markov chain models, simulation algorithms and associated spatial correlation measures based on the Markov chain random field theory, which extends a single Markov chain into a multi-dimensional field for geostatistical modeling. A Markov chain random field...
    
    
  • Markov chain mixing time
    Markov chain mixing time
    In probability theory, the mixing time of a Markov chain is the time until the Markov chain is "close" to its steady state distribution.More precisely, a fundamental result about Markov chains is that a finite state irreducible aperiodic chain has a unique stationary distribution π and,...
    
    
• Markov chain Monte Carlo
  Markov chain Monte Carlo
  Markov chain Monte Carlo methods are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution. The state of the chain after a large number of steps is then used as a sample of the...
  
  
• Markov decision process
  Markov decision process
  Markov decision processes , named after Andrey Markov, provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying a wide range of optimization problems solved via...
  
  
• Markov information source
  Markov information source
  In mathematics, a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain.-Formal definition:...
  
  
• Markov kernel
  Markov kernel
  In probability theory, a Markov kernel is a map that plays the role, in the general theory of Markov processes, that the transition matrix does in the theory of Markov processes with a finite state space.- Formal definition :...
  
  
• Markov logic network
  Markov logic network
  A Markov logic network is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference...
  
  
• Markov model
  Markov model
  In probability theory, a Markov model is a stochastic model that assumes the Markov property. Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable.-Introduction:...
  
  
• Markov network
  Markov network
  A Markov random field, Markov network or undirected graphical model is a set of variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies...
  
  
• Markov process
  Markov process
  In probability theory and statistics, a Markov process, named after the Russian mathematician Andrey Markov, is a time-varying random phenomenon for which a specific property holds...
  
  
• Markov property
  Markov property
  In probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process. It was named after the Russian mathematician Andrey Markov....
  
  
• Markov random field
• Markov's inequality
  Markov's inequality
  In probability theory, Markov's inequality gives an upper bound for the probability that a non-negative function of a random variable is greater than or equal to some positive constant...
  
  
• Markovian arrival processes
  Markovian arrival processes
  In queueing theory, Markovian arrival processes are used to model the arrival of customers to a queue.Some of the most common include the Poisson process, Markov arrival process and the batch Markov arrival process.-Background:...
  
  
• Marsaglia polar method
  Marsaglia polar method
  The polar method is a pseudo-random number sampling method for generating a pair of independent standard normal random variables...
  
  
• Martingale (probability theory)
  Martingale (probability theory)
  In probability theory, a martingale is a model of a fair game where no knowledge of past events can help to predict future winnings. In particular, a martingale is a sequence of random variables for which, at a particular time in the realized sequence, the expectation of the next value in the...
  
  
• Martingale difference sequence
  Martingale difference sequence
  In probability theory, a martingale difference sequence is related to the concept of the martingale. A stochastic series Y is an MDS if its expectation with respect to past values of another stochastic series X is zero...
  
  
• Martingale representation theorem
  Martingale representation theorem
  In probability theory, the martingale representation theorem states that a random variable which is measurable with respect to the filtration generated by a Brownian motion can be written in terms of an Itô integral with respect to this Brownian motion....
  
  
• Master equation
  Master equation
  In physics and chemistry and related fields, master equations are used to describe the time-evolution of a system that can be modelled as being in exactly one of countable number of states at any given time, and where switching between states is treated probabilistically...
  
  
• Matched filter
  Matched filter
  In telecommunications, a matched filter is obtained by correlating a known signal, or template, with an unknown signal to detect the presence of the template in the unknown signal. This is equivalent to convolving the unknown signal with a conjugated time-reversed version of the template...
  
  
• Matching pursuit
  Matching pursuit
  Matching pursuit is a type of numerical technique which involves finding the "best matching" projections of multidimensional data onto an over-complete dictionary D...
  
  
• Matching (statistics)
  Matching (statistics)
  Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated in non experimental design . People use this technique with observational data...
  
  
• Matérn covariance function
  Matérn covariance function
  In statistics, the Matérn covariance is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces...
  
  
• Mathematica
  Mathematica
  Mathematica is a computational software program used in scientific, engineering, and mathematical fields and other areas of technical computing...
  
   – software
• Mathematical biology
  Mathematical biology
  Mathematical and theoretical biology is an interdisciplinary scientific research field with a range of applications in biology, medicine and biotechnology...
  
  
• Mathematical modelling in epidemiology
  Mathematical modelling in epidemiology
  It is possible to mathematically model the progress of most infectious diseases to discover the likely outcome of an epidemic or to help manage them by vaccination...
  
  
• Mathematical modelling of infectious disease
• Mathematical statistics
  Mathematical statistics
  Mathematical statistics is the study of statistics from a mathematical standpoint, using probability theory as well as other branches of mathematics such as linear algebra and analysis...
  
  
• Matthews correlation coefficient
  Matthews Correlation Coefficient
  The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes...
  
  
• Matrix normal distribution
  Matrix normal distribution
  The matrix normal distribution is a probability distribution that is a generalization of the normal distribution to matrix-valued random variables.- Definition :...
  
  
• Matrix population models
  Matrix population models
  Population models are used in population ecology to model the dynamics of wildlife or human populations. Matrix population models are a specific type of population model that uses matrix algebra...
  
  
• Mauchly's sphericity test
  Mauchly's sphericity test
  Mauchly's sphericity test is a statistical test used to validate repeated measures factor ANOVAs. The test was introduced by ENIAC co-inventor John Mauchly in 1940.-What is sphericity?:...
  
  
• Maximal ergodic theorem
• Maximum a posteriori estimation
• Maximum entropy classifier redirects to Logistic regression
  Logistic regression
  In statistics, logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression...
  
  
• Maximum entropy Markov model
  Maximum entropy Markov model
  In machine learning, a maximum-entropy Markov model , or conditional Markov model , is a graphical model for sequence labeling that combines features of hidden Markov models and maximum entropy models...
  
  
• Maximum entropy method redirects to Principle of maximum entropy
  Principle of maximum entropy
  In Bayesian probability, the principle of maximum entropy is a postulate which states that, subject to known constraints , the probability distribution which best represents the current state of knowledge is the one with largest entropy.Let some testable information about a probability distribution...
  
  
• Maximum entropy probability distribution
  Maximum entropy probability distribution
  In statistics and information theory, a maximum entropy probability distribution is a probability distribution whose entropy is at least as great as that of all other members of a specified class of distributions....
  
  
• Maximum entropy spectral estimation
  Maximum entropy spectral estimation
  The maximum entropy method applied to spectral density estimation. The overall idea is that the maximum entropy rate stochastic process that satisfies the given constant autocorrelation and variance constraints, is a linear Gauss-Markov process with i.i.d...
  
  
• Maximum likelihood
  Maximum likelihood
  In statistics, maximum-likelihood estimation is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters....
  
  
• Maximum likelihood sequence estimation
  Maximum Likelihood Sequence Estimation
  Maximum likelihood sequence estimation is a mathematical algorithm to extract useful data out of a noisy data stream.-Theory:For an optimized detector for digital signals the priority is not to reconstruct the transmitter signal, but it should do a best estimation of the transmitted data with the...
  
  
• Maximum parsimony
  Maximum parsimony
  Parsimony is a non-parametric statistical method commonly used in computational phylogenetics for estimating phylogenies. Under parsimony, the preferred phylogenetic tree is the tree that requires the least evolutionary change to explain some observed data....
  
  
• Maximum spacing estimation
  Maximum spacing estimation
  In statistics, maximum spacing estimation , or maximum product of spacing estimation , is a method for estimating the parameters of a univariate statistical model...
  
  
• Maxwell speed distribution
  Maxwell Speed Distribution
  Classically, an ideal gas' molecules bounce around with somewhat arbitrary velocities, never interacting with each other. In reality, however, an ideal gas is subjected to intermolecular forces. It is to be noted that the aforementioned classical treatment of an ideal gas is only useful when...
  
  
• Maxwell–Boltzmann distribution
• Maxwell’s theorem
• MCAR
  MCAR
  In statistical analysis, data-values in a data set are missing completely at random if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest....
  
   (missing completely at random)
• McCullagh's parametrization of the Cauchy distributions
• McDiarmid's inequality
• McDonald–Kreitman test — statistical genetics
• McNemar's test
  McNemar's test
  In statistics, McNemar's test is a non-parametric method used on nominal data. It is applied to 2 × 2 contingency tables with a dichotomous trait, with matched pairs of subjects, to determine whether the row and column marginal frequencies are equal...
  
  
• Meadow's law
  Meadow's law
  Meadow's Law was a precept much in use until recently in the field of child protection, specifically by those investigating cases of multiple cot or crib death — SIDS — within a single family.-History:...
  
  
• Mean
  Mean
  In statistics, mean has two related meanings:* the arithmetic mean .* the expected value of a random variable, which is also called the population mean....
  
  
• Mean – see also expected value
  Expected value
  In probability theory, the expected value of a random variable is the weighted average of all possible values that this random variable can take on...
  
  
• Mean absolute error
  Mean absolute error
  In statistics, the mean absolute error is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by...
  
  
• Mean absolute percentage error
  Mean Absolute Percentage Error
  Mean absolute percentage error is measure of accuracy in a fitted time series value in statistics, specifically trending. It usually expresses accuracy as a percentage, and is defined by the formula:...
  
  
• Mean absolute scaled error
  Mean absolute scaled error
  In statistics, the mean absolute scaled error is a measure of the accuracy of forecasts . It was proposed in 2006 by Australian statistician Rob Hyndman, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements."The mean...
  
  
• Mean and predicted response
  Mean and predicted response
  In linear regression mean response and predicted response are values of the dependent variable calculated from the regression parameters and a given value of the independent variable...
  
  
• Mean deviation
• Mean difference
  Mean difference
  The mean difference is a measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution. A related statistic is the relative mean difference, which is the mean difference divided by the arithmetic mean...
  
  
• Mean integrated squared error
• Mean of circular quantities
  Mean of circular quantities
  In mathematics, a mean of circular quantities is a mean which is suited for quantities like angles, daytimes, and fractional parts of real numbers. This is necessary since most of the usual means fail on circular quantities...
  
  
• Mean percentage error
  Mean Percentage Error
  In statistics, the mean percentage error is the computed average of percentage errors by which estimated forecasts differ from actual values of the quantity being forecast.Formula for mean percentage error calculation is:...
  
  
• Mean preserving spread
• Mean reciprocal rank
  Mean reciprocal rank
  Mean reciprocal rank is a statistic for evaluating any process that produces a list of possible responses to a query, ordered by probability of correctness. The reciprocal rank of a query response is the multiplicative inverse of the rank of the first correct answer...
  
  
• Mean signed difference
• Mean square quantization error
  Mean square quantization error
  Mean square quantization error is a figure of merit for the process of analog to digital conversion.As the input is varied, the input's value is recorded when the digital output changes. For each digital output, the input's difference from ideal is normalized to the value of the least significant...
  
  
• Mean square weighted deviation
  Mean square weighted deviation
  Mean square weighted deviation is used extensively in geochronology, the science of obtaining information about the time of formation of, for example, rocks, minerals, bones, corals, or charcoal, or the time at which particular processes took place in a rock mass, for example recrystallization and...
  
  
• Mean squared error
  Mean squared error
  In statistics, the mean squared error of an estimator is one of many ways to quantify the difference between values implied by a kernel density estimator and the true values of the quantity being estimated. MSE is a risk function, corresponding to the expected value of the squared error loss or...
  
  
• Mean squared prediction error
• Mean time between failures
• Mean-reverting process — redirects to Ornstein–Uhlenbeck process
• Mean value analysis
  Mean value analysis
  In queueing theory, a specialty within the mathematical theory of probability, mean value analysis is a technique for computing expected queue lengths in equilibrium for a closed separable system of queues...
  
  
• Measurement, level of — see level of measurement
  Level of measurement
  The "levels of measurement", or scales of measure are expressions that typically refer to the theory of scale types developed by the psychologist Stanley Smith Stevens. Stevens proposed his theory in a 1946 Science article titled "On the theory of scales of measurement"...
  
  .
• MedCalc
  MedCalc
  MedCalc is a statistical software package designed for the biomedical sciences. It has an integrated spreadsheet for data input and can import files in several formats...
  
   – software
• Median
  Median
  In probability theory and statistics, a median is described as the numerical value separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to...
  
  
• Median absolute deviation
  Median absolute deviation
  In statistics, the median absolute deviation is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample....
  
  
• Median polish
  Median polish
  The median polish is an exploratory data analysis procedure proposed by the statistician John Tukey. It finds an additively-fit model for data in a two-way layout table of the form row effect + column effect + overall median.-References:* Frederick Mosteller and John Tukey . "Data Analysis and...
  
  
• Median test
  Median test
  In statistics, Mood's median test is a special case of Pearson's chi-squared test. It is a nonparametric test that tests the null hypothesis that the medians of the populations from which two samples are drawn are identical...
  
  
• Mediation (statistics)
  Mediation (Statistics)
  In statistics, a mediation model is one that seeks to identify and explicate the mechanism that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable...
  
  
• Medical statistics
  Medical statistics
  Medical statistics deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research...
  
  
• Medoid
  Medoid
  Medoids are representative objects of a data set or a cluster with a data set whose average dissimilarity to all the objects in the cluster is minimal. Medoids are similar in concept to means or centroids, but medoids are always members of the data set...
  
  
• Memorylessness
  Memorylessness
  In probability and statistics, memorylessness is a property of certain probability distributions: the exponential distributions of non-negative real numbers and the geometric distributions of non-negative integers....
  
  
• Mendelian randomization
  Mendelian randomization
  In epidemiology, Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies...
  
  
• Mentor (statistics)
  Mentor (statistics)
  Mentor is a flexible and sophisticated statistical analysis system produced by CfMC. It specializes in the tabulation and graphical display of market and opinion research data, and is integrated with their Survent data collection software....
  
   – software
• Meta-analysis
  Meta-analysis
  In statistics, a meta-analysis combines the results of several studies that address a set of related research hypotheses. In its simplest form, this is normally by identification of a common measure of effect size, for which a weighted average might be the output of a meta-analyses. Here the...
  
  
• Meta-analytic thinking
• Method of moments (statistics)
• Method of simulated moments
  Method of simulated moments
  In econometrics, the method of simulated moments is a structural estimation technique introduced by Daniel McFadden. It extends the generalized method of moments to cases where theoretical moment functions cannot be evaluated directly, such as when moment functions involve high-dimensional...
  
  
• Method of support
  Method of support
  In statistics, the method of support is a technique that is used to make inferences from datasets.According to A. W. F. Edwards, the method of support aims to make inferences about unknown parameters in terms of the relative support, or log likelihood, induced by a set of data for a particular...
  
  
• Metropolis–Hastings algorithm
• Mexican paradox
  Mexican paradox
  The Mexican paradox is the observation that the Mexican people exhibit a surprisingly low incidence of low birth mass, contrary to what would be expected from their socioeconomic status...
  
  
• Microdata (statistics)
  Microdata (statistics)
  In the study of survey and census data, microdata is information at the level of individual respondents. For instance, a national census might collect age, home address, educational level, employment status, and many other variables, recorded separately for every person who responds; this is...
  
  
• Midhinge
  Midhinge
  In statistics, the midhinge is the average of the first and third quartiles and is thus a measure of location.Equivalently, it is the 25% trimmed mid-range; it is an L-estimator....
  
  
• Mid-range
• MinHash
  MinHash
  In computer science, MinHash is a technique for quickly estimating how similar two sets are...
  
  
• Minimax
  Minimax
  Minimax is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss for a worst case scenario. Alternatively, it can be thought of as maximizing the minimum gain...
  
  
• Minimax estimator
• Minimisation (clinical trials)
• Minimum distance estimation
  Minimum distance estimation
  Minimum distance estimation is a statistical method for fitting a mathematical model to data, usually the empirical distribution.-Definition:...
  
  
• Minimum mean square error
• Minimum-variance unbiased estimator
  Minimum-variance unbiased estimator
  In statistics a uniformly minimum-variance unbiased estimator or minimum-variance unbiased estimator is an unbiased estimator that has lower variance than any other unbiased estimator for all possible values of the parameter.The question of determining the UMVUE, if one exists, for a particular...
  
  
• Minimum viable population
  Minimum Viable Population
  Minimum viable population is a lower bound on the population of a species, such that it can survive in the wild. This term is used in the fields of biology, ecology, and conservation biology...
  
  
• Minitab
  Minitab
  Minitab is a statistics package. It was developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in 1972...
  
  
• MINQUE
  Minque
  In statistics, the theory of minimum norm quadratic unbiased estimation was developed by C.R. Rao. Its application was originally to the estimation of variance components in random effects models.The theory involves three stages:...
  
   – minimum norm quadratic unbiased estimation
• Missing completely at random
• Missing data
• Missing values — redirects to Missing data
• Mittag–Leffler distribution
• Mixed logit
  Mixed logit
  Mixed logit is a fully general statistical model for examining discrete choices. The motivation for the mixed logit model arises from the limitations of the standard logit model...
  
  
• Misuse of statistics
  Misuse of statistics
  A misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false...
  
  
• Mixed data sampling
  Mixed data sampling
  Mixed data sampling is an econometric regression or filtering method developed by Ghysels et al. A simple regression example has the regressor appearing at a higher frequency than the regressand:...
  
  
• Mixed-design analysis of variance
  Mixed-design analysis of variance
  In statistics, a mixed-design analysis of variance model is used to test for differences between two or more independent groups whilst subjecting participants to repeated measures...
  
  
• Mixed model
  Mixed model
  A mixed model is a statistical model containing both fixed effects and random effects, that is mixed effects. These models are useful in a wide variety of disciplines in the physical, biological and social sciences....
  
  
• Mixing (mathematics)
  Mixing (mathematics)
  In mathematics, mixing is an abstract concept originating from physics: the attempt to describe the irreversible thermodynamic process of mixing in the everyday world: mixing paint, mixing drinks, etc....
  
  
• Mixture distribution
• Mixture model
  Mixture model
  In statistics, a mixture model is a probabilistic model for representing the presence of sub-populations within an overall population, without requiring that an observed data-set should identify the sub-population to which an individual observation belongs...
  
  
• Mixture (probability)
  Mixture (probability)
  In probability theory and statistics, a mixture is a combination of two or more probability distributions. The concept arises in two contexts:* A mixture defining a new probability distribution from some existing ones, as in a mixture density...
  
  
• MLwiN
  MLwiN
  MLwiN is a statistical software package for fitting multilevel models. It uses both maximum likelihood estimation and Markov Chain Monte Carlo methods...
  
  
• Mode (statistics)
  Mode (statistics)
  In statistics, the mode is the value that occurs most frequently in a data set or a probability distribution. In some fields, notably education, sample data are often called scores, and the sample mode is known as the modal score....
  
  
• Model output statistics
  Model output statistics
  Model Output Statistics is an omnipresent statistical technique that forms the backbone of modern weather forecasting. The technique pioneered in the 1960s and early 1970s is used to post-process output from numerical weather forecast models...
  
  
• Model selection
  Model selection
  Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered...
  
  
• Moderator variable redirects to Moderation (statistics)
  Moderation (statistics)
  In statistics, moderation occurs when the relationship between two variables depends on a third variable. The third variable is referred to as the moderator variable or simply the moderator...
  
  
• Modifiable areal unit problem
  Modifiable Areal Unit Problem
  The modifiable areal unit problem is a source of statistical bias that can radically affect the results of statistical hypothesis tests. It affects results when point-based measures of spatial phenomena are aggregated into districts. The resulting summary values are influenced by the choice of...
  
  
• Moffat distribution
  Moffat distribution
  The Moffat distribution, named after the physicist Anthony Moffat, is a continuous probability distribution based upon the Lorentzian distribution...
  
  
• Moment (mathematics)
  Moment (mathematics)
  In mathematics, a moment is, loosely speaking, a quantitative measure of the shape of a set of points. The "second moment", for example, is widely used and measures the "width" of a set of points in one dimension or in higher dimensions measures the shape of a cloud of points as it could be fit by...
  
  
• Moment-generating function
  Moment-generating function
  In probability theory and statistics, the moment-generating function of any random variable is an alternative definition of its probability distribution. Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or...
  
  
• Moments, method of — see method of moments (statistics)
• Moment problem
• Monotone likelihood ratio
• Monte Carlo integration
• Monte Carlo method
  Monte Carlo method
  Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in computer simulations of physical and mathematical systems...
  
  
• Monte Carlo method for photon transport
  Monte Carlo method for photon transport
  Modeling photon propagation with Monte Carlo methods is a flexible yet rigorous approach to simulate photon transport. In the method, local rules of photon transport are expressed as probability distributions which describe the step size of photon movement between sites of photon-tissue interaction...
  
  
• Monte Carlo methods for option pricing
• Monte Carlo methods in finance
  Monte Carlo methods in finance
  Monte Carlo methods are used in finance and mathematical finance to value and analyze instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining their average value over the range of resultant outcomes. This is usually done...
  
  
• Monte Carlo molecular modeling
  Monte Carlo molecular modeling
  Monte Carlo molecular modeling is the application of Monte Carlo methods to molecular problems. These problems can also be modeled by the molecular dynamics method. The difference is that this approach relies on statistical mechanics rather than molecular dynamics. Instead of trying to reproduce...
  
  
• Moral graph
  Moral graph
  A moral graph is a concept in graph theory, used to find the equivalent undirected form of a directed acyclic graph. It is a key step of the junction tree algorithm, used in belief propagation on graphical models....
  
  
• Moran process
  Moran process
  A Moran process, named after Patrick Moran, is a stochastic process used in biology to describe finite populations. It can be used to model variety-increasing processes such as mutation as well as variety-reducing effects such as genetic drift and natural selection...
  
  
• Moran's I
  Moran's I
  In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick A.P. Moran. Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial...
  
  
• Morisita's overlap index
• Morris method
  Morris method
  In applied statistics, the Morris method for global sensitivity analysis is a so-called one-step-at-a-time method , meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r of local changes at different points x of the...
  
  
• Mortality rate
  Mortality rate
  Mortality rate is a measure of the number of deaths in a population, scaled to the size of that population, per unit time...
  
  
• Most probable number
  Most probable number
  The most probable number method, otherwise known as the method of Poisson zeroes, is a method of getting quantitative data on concentrations of discrete items from positive/negative data....
  
  
• Moving average
• Moving average model
  Moving average model
  In time series analysis, the moving-average model is a common approach for modeling univariate time series models. The notation MA refers to the moving average model of order q:...
  
  
• Moving average representation — redirects to Wold's theorem
• Moving least squares
  Moving least squares
  Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested....
  
  
• Multi-armed bandit
  Multi-armed bandit
  In statistics, particularly in the design of sequential experiments, a multi-armed bandit takes its name from a traditional slot machine . Multiple levers are considered in the motivating applications in statistics. When pulled, each lever provides a reward drawn from a distribution associated...
  
  
• Multi-vari chart
  Multi-vari chart
  In quality control, multi-vari charts are a visual way of presenting variability through a series of charts. The content and format of the charts has evolved over time.-Original concept:...
  
  
• Multiclass classification
  Multiclass classification
  In machine learning, multiclass or multinomial classification is the problem of classifying instances into more than two classes.While some classification algorithms naturally permit the use of more than two classes, others are by nature binary algorithms; these can, however, be turned into...
  
  
• Multiclass LDA (Linear discriminant analysis) — redirects to Linear discriminant analysis
  Linear discriminant analysis
  Linear discriminant analysis and the related Fisher's linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events...
  
  
• Multicollinearity
  Multicollinearity
  Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data...
  
  
• Multidimensional analysis
  Multidimensional analysis
  In statistics, econometrics, and related fields, multidimensional analysis is a data analysis process that groups data into two or more categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a...
  
  
• Multidimensional Chebyshev's inequality
  Multidimensional Chebyshev's inequality
  In probability theory, the multidimensional Chebyshev's inequality is a generalization of Chebyshev's inequality, which puts a bound on the probability of the event that a random variable differs from its expected value by more than a specified amount....
  
  
• Multidimensional panel data
  Multidimensional panel data
  In econometrics, panel data is data observed over two dimensions . A panel data set is termed "multidimensional" when the phenomenon is observed over three or more dimensions...
  
  
• Multidimensional scaling
  Multidimensional scaling
  Multidimensional scaling is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities, then assigns a location to each...
  
  
• Multifactor design of experiments software
  Multifactor design of experiments software
  Software that is used for designing factorial experiments plays an important role in scientific experiments generally and represents a route to the implementation of design of experiments procedures that derive from statistical and combinatoric theory...
  
  
• Multifactor dimensionality reduction
  Multifactor dimensionality reduction
  Multifactor dimensionality reduction is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable...
  
  
• Multilevel model
  Multilevel model
  Multilevel models are statistical models of parameters that vary at more than one level...
  
  
• Multinomial distribution
• Multinomial logit
  Multinomial logit
  In statistics, economics, and genetics, a multinomial logit model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes...
  
  
• Multinomial probit
  Multinomial probit
  In econometrics and statistics, the multinomial probit model, a popular alternative to the multinomial logit model, is a generalization of the probit model that allows more than two discrete, unordered outcomes. It is not to be confused with the multivariate probit model, which is used to model...
  
  
• Multinomial test
  Multinomial test
  In statistics, the multinomial test is the test of the null hypothesis that the parameters of a multinomial distribution equal specified values. It is used for categorical data; see Read and Cressie....
  
  
• Multiple baseline design
  Multiple Baseline Design
  A multiple baseline design is a style of research involving the careful measurement of multiple persons, traits or settings both before and after a treatment. This design is used in medical, psychological and biological research to name a few areas. It has several advantages over AB designs which...
  
  
• Multiple comparisons
  Multiple comparisons
  In statistics, the multiple comparisons or multiple testing problem occurs when one considers a set of statistical inferences simultaneously. Errors in inference, including confidence intervals that fail to include their corresponding population parameters or hypothesis tests that incorrectly...
  
  
• Multiple correlation
  Multiple correlation
  In statistics, multiple correlation is a linear relationship among more than two variables. It is measured by the coefficient of multiple determination, denoted as R2, which is a measure of the fit of a linear regression...
  
  
• Multiple correspondence analysis
  Multiple correspondence analysis
  In statistics, multiple correspondence analysis is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space. The procedure thus appears to be the...
  
  
• Multiple discriminant analysis
  Multiple discriminant analysis
  Multiple Discriminant Analysis is a method for compressing a multivariate signal to yield a lower dimensional signal amenable to classification....
  
  
• Multiple-indicator kriging
  Multiple-indicator kriging
  Multiple-indicator kriging is a recent advance on other techniques for mineral deposit modeling and resource block model estimation, such as ordinary kriging....
  
  
• Multiple Indicator Cluster Survey
  Multiple Indicator Cluster Survey
  The Multiple Indicator Cluster Surveys are a survey program developed by the United Nations Children's Fund to provide internationally comparable, statistically rigorous data on the situation of children and women. The first round of surveys was carried out in over 60 countries in 1995 in...
  
  
• Multiple of the median
  Multiple of the median
  A multiple of the median is a measure of how far an individual test result deviates from the median. MoM is commonly used to report the results of medical screening tests, particularly where the results of the individual tests are highly variable....
  
  
• Multiple testing correction redirects to Multiple comparisons
  Multiple comparisons
  In statistics, the multiple comparisons or multiple testing problem occurs when one considers a set of statistical inferences simultaneously. Errors in inference, including confidence intervals that fail to include their corresponding population parameters or hypothesis tests that incorrectly...
  
  
• Multiple-try Metropolis
  Multiple-try Metropolis
  In Markov chain Monte Carlo, the Metropolis–Hastings algorithm can be used to sample from a probability distribution which is difficult to sample from directly. However, the MH algorithm requires the user to supply a proposal distribution, which can be relatively arbitrary...
  
  
• Multiresolution analysis
  Multiresolution analysis
  A multiresolution analysis or multiscale approximation is the design method of most of the practically relevant discrete wavelet transforms and the justification for the algorithm of the fast wavelet transform...
  
  
• Multiscale decision making
  Multiscale decision making
  Multiscale decision making, also referred to as Multiscale decision theory , is a recently developed approach in operations research that fuses game theory, multi-agent influence diagrams, in particular dependency graphs, and Markov decision processes to solve multiscale challenges across...
  
  
• Multiscale geometric analysis
  Multiscale geometric analysis
  Multiscale geometric analysis or geometric multiscale analysis is an emerging area of high-dimensional signal processing and data analysis.-See also:*Wavelet*Scale space*Multi-scale approaches*Multiresolution analysis*Singular value decomposition...
  
  
• Multistage testing
  Multistage testing
  Multistage testing is an algorithm-based approach to administering tests. It is very similar to computer-adaptive testing in that items are interactively selected for each examinee by the algorithm, but rather than selecting individual items, groups of items are selected, building the test in stages...
  
  
• Multitrait-multimethod matrix
  Multitrait-multimethod matrix
  The multitrait-multimethod matrix is an approach to examining Construct Validity developed by Campbell and Fiske. There are six major considerations when examining a construct's validity through the MTMM matrix, which are as follows:...
  
  
• Multivariate adaptive regression splines
  Multivariate adaptive regression splines
  Multivariate adaptive regression splines is a form of regression analysis introduced by Jerome Friedman in 1991. It is a non-parametric regression techniqueand can be seen as an extension of linear models that...
  
  
• Multivariate analysis
  Multivariate analysis
  Multivariate analysis is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical variable at a time...
  
  
• Multivariate analysis of variance
• Multivariate distribution – redirects to Joint probability distribution
• Multivariate kernel density estimation
  Multivariate kernel density estimation
  Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density estimation with improved statistical properties...
  
  
• Multivariate normal distribution
• Multivariate Pólya distribution
  Multivariate Polya distribution
  The multivariate Pólya distribution, named after George Pólya, also called the Dirichlet compound multinomial distribution, is a compound probability distribution, where a probability vector p is drawn from a Dirichlet distribution with parameter vector \alpha, and a set of discrete samples is...
  
  
• Multivariate probit
  Multivariate probit
  In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly...
  
  
• Multivariate random variable
  Multivariate random variable
  In mathematics, probability, and statistics, a multivariate random variable or random vector is a list of mathematical variables each of whose values is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value.More formally, a multivariate random...
  
  
• Multivariate stable distribution
• Multivariate statistics
  Multivariate statistics
  Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one statistical variable. The application of multivariate statistics is multivariate analysis...
  
  
• Multivariate Student distribution