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 minimize overfitting without removing features but rather transforming them instead.
In this chapter, we’re going to cover the following main topics:
- Multiple linear regression
- Feature selection
- Shrinkage methods
- Dimension...
