Use Ordinal Logistic
Regression to model the relationship between a set of predictors and an ordinal response. An ordinal response has three or more outcomes that have an order, such as low, medium, and high. You can include interaction and polynomial terms, nest terms within other terms, and fit different link functions.

For example, a field biologist studies the survival time of salamanders and wants to determine whether survival is related to region and the level of water toxicity. The biologist divides survival time into three categories: less than 10 days, 11 to 30 days, and over 30 days. Because the response is an ordinal variable, the biologist uses ordinal logistic regression.

## Where to find this analysis

To use ordinal logistic regression, choose .

## 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 do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic
Regression.
- If your response variable counts occurrences, such as the number of defects, use Fit Poisson
Model.