— Statistical Methods for Research II
This version has ended; see updated course, below. Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures.
Topics
- linear mixed models
- repeated measures
- generalized linear models
- model selection
- analysis of missing data
- and multiple testing procedures
Topics explicitly listed in official catalogue description
Prerequisites & course information
This version has ended; see updated course, below. Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Grade Mode: Letter. STA 207 — Statistical Methods for Research II (4 units) Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Prerequisite(s): STA 206 ; knowledge of vectors and matrices. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics and Biostatistics graduate students; Pass Two restricted to graduate students. Grade Mode: Letter. This course version is effective from, and including: Winter Quarter 2027. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Grade Mode: Letter. STA 207 — Statistical Methods for Research II (4 units) Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Prerequisite(s): STA 206 ; knowledge of vectors and matrices. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics and Biostatistics graduate students; Pass Two restricted to graduate students. Grade Mode: Letter. This course version is effective from, and including: Winter Quarter 2027.
Sources
- Course catalogue ↗2026-2027