18 OLS as a projection

Lydia discusses the geometric interpretation of Ordinary Least Squares (OLS) regression, explaining why minimizing errors perpendicular to the line of fit ensures it's the 'best fitted' line. She illustrates this with a 2D scatter plot, demonstrating that OLS projects data points onto the line in the direction of X, making residuals orthogonal to the line. Lydia extends this concept to higher dimensions and explains its significance in interpreting OLS coefficients independently of noise. Subscribe to @AxiomTutoring for more insights.