Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
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.
