Building Multiple Regression Models


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, we focus on variable selection, the first step in the process used to build powerful and accurate multiple regression models.


Multiple Linear Regression Model Fuel Economy Partial Model Screen Size Marginal Impact 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media, LLC 2009

Personalised recommendations