Basics of Linear Regression

This lecture introduces the basics of the linear regression model, focusing on Ordinary Least Squares (OLS) as a tool to estimate parameters. It covers the assumptions underlying the model, the properties of OLS estimators, hypothesis testing, confidence intervals, and joint tests of significance. The lecture also discusses the impact of sample size, error term properties, and variable correlation on regression results.