Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
