Generalized Linear/Nonlinear Models (GLZ) - Introductory Overview

The Generalized Linear/Nonlinear Models (GLZ) module is a comprehensive implementation of the General Linear Model. Both linear and nonlinear effects for any number and type of predictor variables on a discrete or continuous dependent variable can be analyzed. Designs can include multiple-degrees-of-freedom effects for categorical predictor variables, single-degree-of-freedom effects for continuous predictor variables, or any combination of effects for continuous and categorical predictor variables. GLZ also implements stepwise and best-subset model-building techniques for any type of design. GLZ uses the maximum likelihood (ML) methods of the generalized linear model to build models and to estimate and test hypotheses about effects in the model.

The Introductory Overview topics (listed below) describe the use of the generalized linear model for analyzing linear and non-linear effects of continuous and categorical predictor variables on a discrete or continuous dependent variable. If you are unfamiliar with the basic methods of regression in linear models, it may be useful to first review the basic information on these topics in Elementary concepts. Discussion of the ways in which the linear regression model is extended by the general linear model can be found in the Introductory Overview of the GLM module.

For additional information about generalized linear model, see also Dobson (1990), Green and Silverman (1994), or McCullagh and Nelder (1989). See also, GLZ - Index and GLZ Examples.

Introductory Overviews

Basic Ideas