Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
