Use Fit Poisson Model to understand the relationship between a set of predictors and a response that describes the number of times an event occurs in a finite observation space. A Poisson response counts events, such as the number of defects detected on an item. You can include interaction and polynomial terms, perform stepwise regression, fit different link functions, and validate the model with a test sample or with cross-validation.

For example, a circuit board manufacturer wants to model the number of soldering defects on a circuit board.

After you perform the analysis, Minitab stores the model so that you can do the following:

- Predict the response for new observations.
- Plot the relationships among the variables.
- Find values that optimize one or more responses.

To fit a Poisson regression model, choose .

- 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 contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.