In Chapter 11, we introduced simple linear regression where the mean of a continuous response variable was represented as a linear function of a single predictor variable. In this chapter, this regression scenario is generalized in several ways. In Section 12.2, the multiple regression setting is considered where the mean of a continuous response is written as a function of several predictor variables. Methodology for comparing different regression models is described in Section 12.3. The second generalization considers the case where the response variable is binary with two possible responses in Section 12.4. Here one is interested in modeling the probability of a particular response as a function of an predictor variable. Although these situations are more sophisticated, the Bayesian methodology for inference and prediction follows the general approach described in the previous chapters.