In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
Simply so, what is the difference between general linear model and generalized linear model?
The general linear model requires that the response variable follows the normal distribution whilst the generalized linear model is an extension of the general linear model that allows the specification of models whose response variable follows different distributions.
Beside above, what does a general linear model tell you?
The General Linear Model (GLM) is a useful framework for comparing how several variables affect different continuous variables. In it's simplest form, GLM is described as: Data = Model + Error (Rutherford, 2001, p.3) GLM is the foundation for several statistical tests, including ANOVA, ANCOVA and regression analysis.
What is GEE analysis?
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes. Parameter estimates from the GEE are consistent even when the covariance structure is misspecified, under mild regularity conditions.
Is multiple regression A multivariate analysis?
A multiple regression has more than one X in one formula. A multivariate regression has more than one Y, but in different formulae. And a multivariate multiple regression has multiple X's to predict multiple Y's with each Y in a different formula, usually based on the same data.