Building Statistical Models in Python

7

Multiple Linear Regression

In the last chapter, we discussed simple linear regression (SLR) using one variable to explain a target variable. In this chapter, we will discuss multiple linear regression (MLR), which is a model that leverages multiple explanatory variables to model a response variable. Two of the major conundrums facing multivariate modeling are multicollinearity and the bias-variance trade-off. Following an overview of MLR, we will provide an induction into the methodologies used for evaluating and minimizing multicollinearity. We will then discuss methods for leveraging the bias-variance trade-off to our benefit as analysts. Finally, we will discuss handling multicollinearity using Principal Component Regression (PCR) to