Model Building: Linear Regression

This lecture covers the process of model building in linear regression, focusing on techniques like forward and backward stepwise regression, automatic model selection, and dealing with multicollinearity. It discusses methods for model selection, including prediction error, AIC, BIC, and Mallows' Cp statistic. The lecture also explores the concept of ridge regression as a solution to multicollinearity issues, emphasizing the importance of selecting the most appropriate model to avoid bias and variance problems.