Spring 2027

Regression Methods for Population Health

This course introduces primary statistical tools for epidemiology and health services research, covering logistic, linear, multinomial, and Poisson regression models for various outcome types.

offering recorded4 credits
Recorded instructors · Spring 2027 Mary Baumann

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.58 GPA, 69.2% A/AB (n=13 letter grades); Spring 2025: 3.74 GPA, 88.2% A/AB (n=17 letter grades); Spring 2026: 3.47 GPA, 73.7% A/AB (n=19 letter grades).

What you’ll be able to do

  • State the assumptions underlying linear, logistic, multinomial and Poisson regression models, recognize and address violations of those assumptions, and estimate and interpret regression models to answer epidemiologic and public health research questions.

  • Critique uses of linear, logistic, multinomial and Poisson regression models in the epidemiologic and public health literature.

  • Translate epidemiologic concepts into statistical modeling assumptions, and explain statistical modeling assumptions in epidemiologic terms.

  • Recognize applications that require methods beyond their expertise, and identify resources to learn about more advanced techniques.

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
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AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT/​B M I 541 or POP HLTH/​B M I 551

BMI/POPHLTH 552 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300
LEC 001Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.58 GPA, 69.2% A/AB (n=13 letter grades); Spring 2025: 3.74 GPA, 88.2% A/AB (n=17 letter grades); Spring 2026: 3.47 GPA, 73.7% A/AB (n=19 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Logistic, linear, multinomial, and Poisson/negative binomial regression models.
  • Assumptions, violations, estimation, and interpretation of regression models in epidemiologic research.

Skills

  • Estimation and interpretation of linear, logistic, multinomial, and Poisson regression models.
  • Critiquing the application of regression models in epidemiologic and public health literature.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

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.

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Selected offering source records
BMI/POPHLTH 552 · Spring 2027

Regression Methods for Population Health

Recorded 2026-10-14
BMI/POPHLTH 552 · Spring 2027

Regression Methods for Population Health

Recorded 2026-10-14
Raw records 
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    "credits_min": 4,
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "fd1beb6baa84d3ff06ab0c9b06137354ae28cdef6bf1746a4b28fa1d49b036f0",
  "requirements_status": "needs_review",
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  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}