STAT-5364: Hierarchical Modeling
Description: Hierarchical modeling techniques as applied to assess data with atypical features, such as non-normal responses (e.g., binary, discrete survival, continuous mixtures), censored/missing observations, multivariate responses, repeated measures, and nested structures. Classical and Bayesian techniques for assessing models. Programming experience in R, S+, or Matlab required.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 2
Average GPA: 3.30 (rounds to B+)
Strict A Rate (No A-) : 36.65%
Average Withdrawal Rate: 0.00%
| Xiaowei Wu | 2022 | 46.6% | 26.6% | 26.6% | 0.0% | 0.0% | 0.0% | 3.30 | 2 |