Generalized Linear ModelsCovers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Nonparametric RegressionCovers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Regression DiagnosticsCovers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Nested Model SelectionExplores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
Linear Regression EssentialsCovers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.