STAT 301 Analysis of Variance and Multivariate Analysis
This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.
Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu.
Prerequisites
Special information
4 Undergraduate credits
Effective May 6, 2018 to present
Meets graduation requirements for
Learning outcomes
General
- Analyze multivariate data using principle component analysis and cluster analysis
- Summarize the analysis of statistical models when applied to specified data sets.
- Select among different ANOVA models for hypothesis testing based on the experimental design.
- Interpret advanced hypothesis testing techniques related to ANOVA.
- Apply statistical principles and methods for analysis of variance (ANOVA).
- Interpret the role of experimental design in controlling for variation among experimental outcomes.
Spring 2026
| Section | Title | Instructor | books | eservices |
|---|---|---|---|---|
| 01 | Analysis of Variance and Multivariate Analysis | Wei, Wei | Books for STAT-301-01 Spring 2026 | Course details for STAT-301-01 Spring 2026 |