Applications in Anova and Mixed Models
Applications in ANOVA and Mixed Models covers developing models and programs for variance analysis, mixed models, repeated measures, and missing data in biological research.
Summary
What you’ll be able to do
Describe the importance of experimental design in research.
Plan and execute a proper advanced experimental design.
Use proper experimental design and analysis tools for most advanced agricultural experiments.
Explain the theory and concepts of mixed models as well as applications of mixed models in biological research settings.
Source: UW Guide
Grade history
↗No recorded grade history.
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Prerequisites
Course map(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)
- F&WECOL/STAT 572 · prior_or_concurrent
- ANSCI 865 · prior_or_concurrent
- HORT 572 prior to Spring 2025
- PLANTSCI 771 · prior_or_concurrent
- AGRONOMY 771 prior to Fall 2025
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- Any of
- F&WECOL/STAT 572 · prior_or_concurrent
- ANSCI 865 · prior_or_concurrent
- HORT 572 prior to Spring 2025
- Any of
- PLANTSCI 771 · prior_or_concurrent
- AGRONOMY 771 prior to Fall 2025
- Any of
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
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Student experience
the class
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.
difficulty & workload
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Topics
- Analysis of variance, mixed models, variance and covariance analyses, repeated measures, and missing data.
- Experimental design theory and application.
Skills
- Developing models and programs for ANOVA, mixed models, variance-covariance analysis, repeated measures, and missing data handling.
- Planning and executing advanced experimental designs.
Grades
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Catalog & offerings
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Raw records
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "9ca63d67261aef393afd133e0a9c506fb43260bf5cbf32a4c3373ebf827f4c4b",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}