Model Comparison in Linear Regression

This lecture delves into the process of comparing different models in linear regression to determine the significance of adding or removing variables. The instructor explains how to assess if a simpler model is as effective as a more complex one by analyzing the reduction in residual sum of squares. Through a step-by-step approach, the lecture covers the concept of orthogonal decomposition of fits, the use of F-tests to evaluate model significance, and the construction of an analysis of variance table to compare model variations. Practical examples and visual aids help illustrate the importance of model comparison in statistical analysis.