Model Checking and ResidualsExplores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Regression: Linear ModelsIntroduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Linear Regression BasicsCovers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Linear Models: Part 1Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Geometry and Least SquaresDiscusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
Nested Model SelectionExplores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.