Generalized Linear Model

In statistics, the generalized linear model (GLM) is a useful generalization of ordinary least squares regression. It relates the random distribution of the measured variable of the experiment (the distribution function) to the systematic (non-random) portion of the experiment (the linear predictor) through a function called the link function.
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The generalized linear model is a generalization of the linear regression model such that (1) nonlinear, as well as linear, effects can be tested (2) for categorical predictor variables , as well as for continuous predictor variables, using (3) any dependent variable whose distribution follows several special members of the exponential family of distributions (e.g., gamma, Possion, binomial, etc.), as well as for any normally-distributed dependent variable.
For an overview of the generalized linear model see the Generalized Linear Models chapter.