Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
