This chapter extends regression topics from Chapter 13’s focus on linear regression with a continuous outcome to the current chapter’s focus on the logistic regression model for a dichotomous outcome. Similar to the chapter on linear regression, this chapter starts with simple logistic regression with extensions to multivariable (multiple predictors) logistic regression models. The chapter includes similarities and differences between the linear and logistic regression modeling approaches. Also described are hypothesis testing for point estimates and confidence intervals of logistic regression model coefficients. Included in the chapter is the demonstration of the use of statistical software (SAS and Stata) to develop logistic regression models with an emphasis on reading output from statistical software and interpreting the results in context. Furthermore, an epidemiologic framework using odds ratios is applied to the interpretation of logistic regression model estimates. The chapter concludes with a discussion on model evaluation and introduces the c statistic used for evaluating the fit of the logistic regression models.