User:Sjcphd/sandbox Comparison of general and generalized linear models

The general linear model (GLM) and the generalized linear model (GLiM) are two commonly used families of statistical methods to relate some number of continuous and/or categorical predictors to a single outcome variable.

The main difference between the two approaches is that the GLM strictly assumes that the residuals will follow a conditionally normal distribution, while the GLiM loosens this assumption and allows for a variety of other distributions from the exponential family for the residuals. Of note, the GLM is a special case of the GLiM in which the distribution of the residuals follow a conditionally normal distribution.

The distribution of the residuals largely depends on the type and distribution of the outcome variable; different types of outcome variables lead to the variety of models within the GLiM family. Commonly used models in the GLiM family include binary logistic regression for binary or dichotomous outcomes, Poisson regression for count outcomes, and linear regression for continuous, normally distributed outcomes. This means that GLiM may be spoken of as a general family of statistical models or as specific models for specific outcome types.

Additional resources


Category:Generalized linear models

Category:Statistical methods Category:General linear model