Building Multiple Regression Models

Explanatory multiple regression models are used to accomplish two complementary goals: identification of drivers of performance and prediction of performance under alternative scenarios. Multiple regression offers a major advantage over simple regression. Multiple regression accounts for the joint impact of multiple drivers, which provides a truer estimate of the impact of each one individually. Looking at just one driver, as in simple regression, its estimated impact will be much greater than it actually is. A single driver takes the credit for the joint influence of multiple drivers. For this reason, multiple regression provides a clearer picture of influence.