ABSTRACT
Linear regression is one of the most widely used statistical tools. This chapter introduces Bayesian model building and inference for normal linear models, focusing on the simple case of linear regression with uniform prior distributions. We apply the hierarchical modeling ideas of Chapter 5 in the context of linear regression in the next chapter. The analysis of the SAT coaching example in Chapter 5 is a special case of hierarchical linear modeling.
