Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
