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.
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