MATH 423 — Applied Regression

Multiple regression estimators and their properties. Hypothesis tests and confidence intervals. Analysis of variance. Prediction and prediction intervals. Model diagnostics. Model selection. Introduction to weighted least squares. Basic contingency table analysis. Introduction to logistic and Poisson regression. Applications to experimental and observational data.

• Rating: 3.90 out of 5 from 236 student reviews
• Difficulty: 2.29 out of 5
• Credits: 3
• Faculty: Faculty of Science
• Department: Mathematics and Statistics
• Taught by: Tharshanna Nadarajah
• Prerequisites: (MATH 209 or MATH 324) and (MATH 206 or MATH 223 or MATH 236)

Sections offered

• Section 001 (Lec), Tue Thu 1:05-2:25 pm — 1 seats open