Chapter Preview. This chapter describes tools and techniques to help you select variables to enter into a linear regression model, beginning with an iterative model selection process. In applications with many potential explanatory variables, automatic variable selection procedures will help you quickly evaluate many models. Nonetheless, automatic procedures have serious limitations including the inability to account properly for nonlinearities such as the impact of unusual points; this chapter expands upon the Chapter 2 discussion of unusual points. It also describes collinearity, a common feature of regression data where explanatory variables are linearly related to one another. Other topics that impact variable selection, including heteroscedasticity and out-of-sample validation, are also introduced.