Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Explores linear regression with and without covariates, covering models captured by independent distributions and tools like subspaces and orthogonal projections.
Explores the analysis of genotypes and variants data through a Genome-Wide Association Study, focusing on the association between genetic variants and phenotypes like height.
Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
