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 linear regression, least squares, residuals, and confidence intervals in regression models.
Covers linear regression for estimating train speed using least squares and regularization.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
