Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
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 advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
