APPLICATIONS IN ANOVA AND MIXED MODELS
Development of models, programs, inferences, and interpretations of analysis of variance in biological research; mixed models and their development; choosing the correct inference range; variance and covariance analyses; repeated measures; dealing with missing data.
Prerequisites & course information
Requisites: ( F&W ECOL/STAT 572 or concurrent enrollment, AN SCI 865 or concurrent enrollment, or HORT 572 prior to Spring 2025) and ( PLANTSCI 771 or concurrent enrollment, or AGRONOMY 771 prior to Fall 2025) Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2024 Learning Outcomes: 1. Describe the importance of experimental design in research. Audience: Graduate 2. Plan and execute a proper advanced experimental design. Audience: Graduate 3. Use proper experimental design and analysis tools for most advanced agricultural experiments. Audience: Graduate 4. Explain the theory and concepts of mixed models as well as applications of mixed models in biological research settings. Audience: Graduate Learning Outcomes: 1. Describe the importance of experimental design in research. Audience: Graduate 2. Plan and execute a proper advanced experimental design. Audience: Graduate 3. Use proper experimental design and analysis tools for most advanced agricultural experiments. Audience: Graduate 4. Explain the theory and concepts of mixed models as well as applications of mixed models in biological research settings. Audience: Graduate
Sources
- Course catalogue ↗2026-2027