Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
