The logistic regression model is the standard technique for building a response model. Its theory is well established, and its estimation algorithm is available in all major statistical software packages. The literature on the theoretical aspects of logistic regression is large and rapidly growing. However, the literature is seemingly empty on the interpretation of the logistic regression model. This chapter presents a data mining method based on chi-squared automatic interaction detection (CHAID) for interpreting a logistic regression model, specifically to provide a complete assessment of the effects of the predictor variables, defining a logistic regression model, on a binary response variable. CHAID is a technique that recursively partitions a population into separate and distinct subpopulations or segments such that the variation of the dependent variable is minimized within the segments and maximized among the segments. A CHAID analysis results in a tree-like diagram, commonly called a CHAID tree.