Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.