Choosing the Right Model
There are usually several models that pass the diagnostic checks, so you need tools to choose between them.
Variable Selection. When constructing a model, you generally want to only include predictors that contribute significantly to the model. The likelihood ratio statistics table tests each variable's contribution to the model. Additionally, the Multinomial Logistic Regression procedure offers several methods for stepwise selection of the "best" predictors to include in the model. See the topic Using Multinomial Logistic Regression to Classify Telecommunications Customers for more information.
Pseudo R-Squared Statistics. The r-squared statistic, which measures the variability in the dependent variable that is explained by a linear regression model, cannot be computed for multinomial logistic regression models. The pseudo r-squared statistics are designed to have similar properties to the true r-squared statistic.
Classification and Validation. Crosstabulating observed response categories with predicted categories helps you to determine how well the model identifies consumer preferences.