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Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.
Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
Covers the essentials of ANOVA, explaining its concept, calculations, assumptions, and interpretation of results.
Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.
