Regression Analysis

Description from DukeHub

Extensive study of regression modeling. Multiple regression, weighted least squares, logistic regression, log-linear models, analysis of variance, model diagnostics, and selection. Emphasis on applications. Examples are drawn from a variety of fields. Prerequisite: 100-level STA course or STA 230, 231, or 240L. Interested students with different backgrounds should discuss and seek instructor consent.

Skills

• data and statisticsdescription: "regression" and 4 more
• lab workhas a lab component

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "diagnostics"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in STA

• Data Analysis and Statistical Inference STA 101L
• Studying Incarceration with Data STA 116CNL
• Introduction to Data Science and Statistical Thinking STA 199CCL
• Regression Analysis: Theory and Applications STA 221L
• Statistical Computing STA 323L
• Statistical Methods for Sports Analytics STA 345

All STA courses · All of Undergraduate