Basics of linear regression model

This lecture introduces the Ordinary Least Squares (OLS) method as an algebraic tool for linear regression. It covers the derivation of the OLS estimator, the Frisch-Waugh-Lovell theorem, predicted values, residuals, matrix notation, and the properties of OLS under Gauss-Markov assumptions. The lecture also explains the concept of goodness-of-fit using the coefficient of determination (R-squared) and discusses hypothesis testing, confidence intervals, and error types in statistical inference.