Building Multiple Regression Models

Abstract

Explanatory multiple regression models are used to accomplish two complementary goals: identification of key drivers of performance and prediction of performance under alternative scenarios. The variables selected affect both the explanatory accuracy and power of models, as well as forecasting precision. In this chapter, the focus is on variable selection, the first step in the process used to build powerful and accurate multiple regression models.