Linear Models: Least Squares

This lecture by the instructor covers the topic of linear models, focusing on the least squares method. It delves into the linear algebra behind linear models, discussing concepts such as projections, non-negative definite matrices, and optimal linear dimension reduction. The lecture explores properties of Gaussian vectors, including moment generating functions, density functions, and isosurfaces. Additionally, it explains how to make a linear model through forward search-stepwise regression and backward elimination-stepwise regression, providing insights into model selection and building. The content emphasizes the importance of selecting the most appropriate subset of variables and the use of objective model comparison criteria.