Free reference · 596 entries
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Short, plain definitions of the terms that turn up in statistics and data science — what they mean, and how they relate to each other. Nothing to sign in for.
45 of 596 entries under M
- Manifest VariableIn latent variable models, a manifest variable (or indicator) is an observable variable – i.e.
- Mann – Whitney U TestSee Wilcoxon – Mann – Whitney Test.
- MANOVASee Multiple analysis of variance
- Mantel-Cox TestThe Mantel-Cox test is aimed at testing the null-hypothesis that survival functions don’t differ across groups.
- Mantel-Haenszel testSee Cochran-Mantel-Haenszel test
- MapReduceIn computer science, MapReduce is a procedure that prepares data for parallel processing on multiple computers.
- Margin of ErrorA margin of error typically refers to a range within which an unknown parameter is estimated to fall, given the variation that can arise from one sample to another.
- Marginal DensityIf X and Y are continuous random variables, and f(x,y ) is the joint density of X and Y, then the marginal density of X, g(x), is given by
- Marginal DistributionIf X and Y are discrete random variables and f(x,y) is their joint probability distribution, the marginal distribution of X, g(x) is given by
- Markov ChainA Markov chain is a series of random values x1, x2, … in which the probabilities associated with a particular value xi depend only on the prior value xi-1.
- Markov Chain (Graphical)A Markov chain is a series of random values x1, x2, … in which the probabilities associated with a particular value xi depend only on the prior value .
- Markov Chain Monte Carlo (MCMC)A Markov chain is a probability system that governs transition among states or through successive events.
- Markov PropertyMarkov property means “absence of memory” of a random process – that is, independence of conditional probabilities P( U(t1 > t) | U(t) ) on values U(t2 < t).
- Markov Property (Graphical)Markov property means “absence of memory” of a random process – that is, independence of conditional probabilities on values U(t2 < t).
- Markov Random FieldSee Markov Chain, Random Field.
- Maximum Likelihood EstimatorThe method of maximum likelihood is the most popular method for deriving estimators – the value of the population parameter T maximizing the likelihood function is used as the estimate of this parameter.
- Maximum Likelihood Estimator (Graphical)The method of maximum likelihood is the most popular method for deriving estimators – the value of the population parameter T maximizing the likelihood function is used as the estimate of this parameter.
- MeanFor a population or a sample, the mean is the arithmetic average of all values. The mean is a measure of central tendency or location.
- Mean DeviationSee Average deviation
- Mean Score StatisticThe mean score statistic is one of the statistics used in the generalized Cochran-Mantel-Haenszel tests.
- Mean Squared ErrorThe mean squared error is a measure of performance of a point estimator. It measures the average squared difference between the estimator and the parameter.
- Mean Values (Comparison)The numerical example below illustrates basic properties of various descriptive statistics with “mean” in their name, like the arithmetic mean, the trimmed mean, the geometric mean, the harmonic mean, and…
- Measurement ErrorThe measurement error is the deviation of the outcome of a measurement from the true value.
- MedianIn a population or a sample, the median is the value that has just as many values above it as below it.
- Median FilterThe median filter is a robust filter. Median filters are widely used as smoothers for image processing, as well as in signal processing and time series processing.
- Meta-analysisMeta-analysis takes the results of two or more studies of the same research question and combines them into a single analysis.
- Minimax Decision RuleA minimax decision rule has the smallest possible maximum risk. All other decision rules will have a higher maximum risk.
- Missing Data Imputation“Imputing missing data” is a process by which the missing values in a data set are estimated from the remaining data, for the purpose of allowing statistical procedures to be performed on a complete data set.
- ModeThe mode is a value that occurs with the greatest frequency in a population or a sample. It could be considered as the single value most typical of all the values.
- Moment Generating FunctionThe moment generation function is associated with a probability distribution. The moment generating function can be used to generate moments.
- MomentsFor a random variable x, its Nth moment is the expected value of the Nth power of x, where N is a positive integer.
- Monte Carlo SimulationMonte Carlo simulation is simulation of a random phenomena using pseudo-random numbers. This type of simulation is widely used in practical statistics, e.g.
- Moving Average (MA) ModelsMoving average (MA) models are used in time series analysis to describe stationary time series.
- MulticollinearityIn regression analysis, multicollinearity refers to a situation of collinearity of independent variables, often involving more than two independent variables, or more than one pair of collinear variables.
- Multidimensional ScalingMultidimensional scaling (MDS) is an approach to multivariate analysis aimed at producing a spatial or geometrical representation of complex data.
- Multiple analysis of covariance (MANCOVA)Multiple analysis of covariance (MANCOVA) is similar to multiple analysis of variance (MANOVA), but allows you to control for the effects of supplementary continuous independent variables – covariates.
- Multiple analysis of variance (MANOVA)MANOVA is a technique which determines the effects of independent categorical variables on multiple continuous dependent variables.
- Multiple ComparisonMultiple comparisons are used in the same context as analysis of variance (ANOVA) – to check whether there are differences in population means among more than two populations.
- Multiple Least Squares RegressionMultiple least squares regression is a special (and the most common) type of multiple regression.
- Multiple looks<b Multiple looks: In a classic statistical experiment, treatment(s) and placebo are applied to randomly assigned subjects, and, at the end of the experiment, outcomes are compared.
- Multiple RegressionMultiple (linear) regression is a regression technique aimed at finding a linear relationship between the dependent variable and multiple independent variables.
- Multiple Regression (Graphical)Multiple (linear) regression is a regression technique aimed at finding a linear relationship between the dependent variable and multiple independent variables.
- Multiple TestingSee Multiple comparison.
- Multiplicative ErrorA multiplicative error is proportional to the true value of the quantity being measured. An example of a multiplicative error is when electronic scales provide readings 1% higher than the true weight – i.e.
- MultivariateMultivariate analysis involves more than one variable of interest.
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