Model Diagnostics: Outliers, Leverage, and Influential Observations

This lecture covers the identification and assessment of outliers, leverage points, and influential observations in statistical models. It discusses methods to detect uncorrelatedness, clustering, and the impact of influential observations on model validity. The instructor explains how to visually check for outliers in regression analysis and the concept of leverage in model fitting. Additionally, the lecture explores Cook's distance for assessing the influence of individual observations and diagnostic plots for model evaluation, including nested model selection. The presentation concludes with residual sums of squares and the comparison of nested models in the context of Gaussian linear models.