ABSTRACT
This chapter provides a treatment of popular Bayesian approaches to working with regression models. It builds on the normal distribution models discussed in Chapter 4. The current chapter once again offers a brief treatment; rather than seeking a comprehensive account, we aim to offer a depth sufficient to support the development of the psychometric models that are the focus of this book. In particular, this chapter focuses on the widely used normal linear model for a continuous outcome variable as it provides a foundation for the development of more complex classical test theory and factor analysis models.
