Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
