Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
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, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
