ABSTRACT
Logistic regression is a popular technique for classifying individuals into two mutually exclusive and exhaustive categories, for example, buyer–nonbuyer and responder–nonresponder. Logistic regression is the workhorse of response modeling as its results are considered the gold standard. This chapter provides a brief overview of the technique and includes a SAS program for building and scoring a logistic regression model (LRM). It presents a case study to demonstrate the building of a response model for an investment product solicitation. The data mining techniques are basic skills that model builders, who are effectively acting like data miners, need to acquire. The LRM belongs to the family of linear models that advance the implied assumption that the underlying relationship between a given predictor variable and the logit is a linear or straight line. The chapter describes two plotting techniques for uncovering the correct reexpression when the bulging rule does not apply.
