Linear and Generalized Linear Models

3 credits · Graduate School

This course is the first in a two-course sequence on intermediate regression methodology. The first half of the course covers ordinary and weighted least squares, analysis of variance (ANOVA), hypothesis testing, confidence and prediction regions, model misspecification, leverage, influence, and diagnostics. The second half of the course covers generalized linear models (e.g., binomial, Poisson, and gamma regression) from the likelihood and Z-estimation frameworks, overdispersion and quasi-likelihood, hypothesis testing, diagnostics, and negative binomial regression. This course emphasizes methodology and computational exploration more than mathematically technical details and theoretical underpinnings. This course is co-located with and serves as repeat credit for BIOS 8345. Prerequisites: BIOS 6312 and BIOS 6342 [3]