Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
