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Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.
