Fitting Linear Models • Generalized Linear Models
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Generalized Linear Models
Fit Models for Nonnormal Response Distributions
Generalized linear models provide a unified way to fit responses that do not fit the usual requirements of traditional linear models. For example, frequency counts are often characterized as having a Poisson distribution and fit using a generalized linear model.
The Generalized Linear Model personality of the Fit Model platform enables you to fit generalized linear models for responses with binomial, normal, Poisson, or exponential distributions. The platform provides reports similar to those that are provided for traditional linear models. The platform also accommodates separation in logistic regression models using the Firth correction.
Figure 11.1 Example of a Generalized Linear Model Fit
Contents 
Overview of the Generalized Linear Model Personality
Example of a Generalized Linear Model
Launch the Generalized Linear Model Personality
Generalized Linear Model Fit Report
Whole Model Test
Generalized Linear Model Fit Report Options
Additional Examples of the Generalized Linear Models Personality
Using Contrasts to Compare Differences in the Levels of a Variable
Poisson Regression with Offset
Normal Regression with a Log Link
Statistical Details for the Generalized Linear Model Personality
Model Selection and Deviance

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Help created on 10/11/2018