Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
