STAT 202A: REGRESSION, ANOVA, AND DESIGN
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): STAT 170; or equivalent; graduate standing; or consent of instructor. Topics include linear regression models; correlations; fitting and prediction; diagnostics; transformations; collinearity; and influential observations. Also addresses model selection; subset selection; Bayesian model selection; regularization; shrinkage methods; and non-parametric and semi-parametric regressions.
Average GPA: 3.76
Grade distribution records: 79 students across 5 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 25 | 31.6% |
| A | 23 | 29.1% |
| A- | 17 | 21.5% |
| B+ | 6 | 7.6% |
| B | 4 | 5.1% |
| B- | 3 | 3.8% |
| C | 1 | 1.3% |
Based on 79 student grade records across 5 terms and 2 professors.
Instructors
- Subir Ghosh 57 students, Average GPA 3.70
- Yuzhou Chen 22 students, Average GPA 3.90