Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Covers linear regression for estimating train speed using least squares and regularization.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
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.
