# 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

Computational Approach

Types of Analyses

Model Building

Zero
Pivot Element Detected During Model Fitting

Interpretation of Results and Diagnostics

See also,
Intrinsically
Nonlinear Regression Models - Models for Binary Responses: Probit &
Logit.