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The general linear model incorporates a number of different statistical models: ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression to the case of more than one dependent variable.
In statistics, a generalized linear model (GLM) 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 be a function of its predicted value.
Linear least squares; Linear model; Linear prediction; Linear probability model; Linear regression; Linguistic demography; Linnik distribution – redirects to Geometric stable distribution; LISREL – proprietary statistical software package; List of basic statistics topics – redirects to Outline of statistics
Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable (DV) are equal across levels of one or more categorical independent variables (IV) and across one or more continuous variables.
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Linear quantile regression models a particular conditional quantile, for example the conditional median, as a linear function β T x of the predictors. Mixed models are widely used to analyze linear regression relationships involving dependent data when the dependencies have a known structure.
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In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. [1] [2] [3] They also inherit from generalized linear models the idea of extending linear mixed models to non-normal data.