This chapter is similar to Chapter 8 but focuses on logistic regression models, which are used in supervised learning contexts where the outcome is binary yes/no. Although we first encountered logistic regression as an example of a classification algorithm in Chapter 2, the math behind it is close to that of linear regression, and software output for the two models is similar. We talk about how to interpret the model coefficients and diagnostics, but save a full treatment of maximum likelihood, deviance residuals, etc. for a later chapter. (22:13; 8 pages)