Advanced Statistical Methods
Advanced Statistical Methods covers modern statistical theory and techniques, including linear and generalized linear regression, random effects, experimental design, and model selection.
Summary
Recent recorded grades — Fall 2023: 3.66 GPA, 88.0% A/AB (n=25 letter grades); Fall 2024: 3.52 GPA, 69.7% A/AB (n=33 letter grades); Fall 2025: 3.64 GPA, 85.7% A/AB (n=7 letter grades).
What you’ll be able to do
Build appropriate regression models given specific contexts.
Conduct estimation and inference for regression models: Calculate important quantities, such as MLE and test statistics. Derive important statistical properties, such as distributions of statistics and conducting hypothesis testing
Analyze the performance of linear models: The use of goodness-of-fit measurements of models. Describe common issues of model building. Use statistical methods to select between models.Provide interpretation of the built models.
Master the concepts and relevant theory for general linear model, especially the inference procedure for binary, multinomial, count and categorical data.
Understand the theory and methods for random effect models and experiment design including blocking, nesting, replication.
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapDeclared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor
- Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2023: 3.66 GPA, 88.0% A/AB (n=25 letter grades); Fall 2024: 3.52 GPA, 69.7% A/AB (n=33 letter grades); Fall 2025: 3.64 GPA, 85.7% A/AB (n=7 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Linear regression models, diagnostic tools, and prediction.
- Generalized linear regression models and random effect models.
- Experimental design and model selection.
Skills
- Application of classical and modern statistical techniques including linear and generalized linear regression, random effects, and experimental design.
- Model selection using statistical methods and interpretation of built models.
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
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": "11446a3d809e603c3737e551fd6808604058a33be2efff4bde39a921ded9a4d2",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}