Use Fit Binary Logistic Model to describe the relationship between a set of predictors and a binary response. A binary response has two outcomes, such as pass or fail. 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, marketers at a cereal company investigate the effectiveness of an ad campaign for a new cereal. The marketers can use binary logistic regression to determine whether people who saw the ad are more likely to buy the cereal.

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

- Predict the probability of an event for new or existing observations.
- Plot the relationships among the variables.
- Find values that optimize multiple responses.

To fit a binary logistic regression model, choose .

- 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 or CART
^{®}Classification. - If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.
- If your data have a missing value pattern that interferes with the construction of the model or if the binary logistic model does not fit well, consider CART
^{®}Classification.