Binary Logistic Regression

Binary logistic regression is most useful when you want to model the event probability for a categorical response variable with two outcomes. For example:

A catalog company wants to increase the proportion of mailings that result in sales.
A doctor wants to accurately diagnose a possibly cancerous tumor.
A loan officer wants to know whether the next customer is likely to default.

To know more, go to Standard Edition>Regression>Binary Logistic Regression

Using the Binary Logistic Regression procedure, the catalog company can send mailings to the people who are most likely to respond, the doctor can determine whether the tumor is more likely to be benign or malignant, and the loan officer can assess the risk of extending credit to a particular customer.