Linear Regression: Beyond the Basics

This lecture covers advanced topics in linear regression models, focusing on properties of OLS estimators, assumptions, and inference. It delves into concepts like multicollinearity, normality assumptions, hypothesis testing, and prediction. The instructor explains the impact of outliers and influential observations on regression results, providing guidance on how to handle them effectively.