Explores statistical estimation, regression models, and model selection in parametric models.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.
Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
