About the Generalized Linear Models Task

Generalized linear models are an extension of traditional linear models. In a generalized linear model, the mean of a population depends on a linear predictor through a nonlinear link function. The response probability distribution can be any member of the exponential family of distributions. Examples of generalized linear models include classical linear models with normal errors, logistic and probit models for binary data, and log-linear models for multinomial data. Other statistical models can be formulated as generalized linear models by the selection of an appropriate link function and response probability distribution.

The Generalized Linear Models task provides model fitting and model building for generalized linear models. It fits models for standard distributions such as Normal, Poisson, and Tweedie in the exponential family. This task also fits multinomial models for ordinal and nominal responses. The task provides forward, backward, and stepwise selection methods.