Overview for Nominal Logistic Regression

Use Nominal Logistic Regression to model the relationship between a set of predictors and a nominal response. A nominal response has three or more outcomes that do not have an order, such as a scratch, dent, and tear. You can include interaction, polynomial, and nested terms.

For example, a school administrator want to investigate the variables that affect a student's preference for certain classes. The administrator uses nominal logistic regression to determine whether a student's age and the teaching method for a class is related to class preference.

Where to find this analysis

To use nominal logistic regression, choose Stat > Regression > Nominal Logistic Regression.

When to use an alternate analysis

If your response variable has two categories, such as pass and fail, use Fit Binary Logistic Model.
If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.