Spring 2027

Applied Regression Analysis

Applied Regression Analysis covers linear regression for prediction, feature selection, and interpretation, extending to mixed and generalized linear models using R.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Karl Rohe

Karl Rohe’s homework was due weekly, long, and unrelated to lectures, while exam questions were vague.

What you’ll be able to do

  • Correctly choose and apply common regression methods that are used in practice to analyze data, including simple and multiple linear regressions, ANOVAs/ANCOVAs, generalized linear models (e.g. logistic and Poisson) and fixed/random/mixed effect models

  • Identify the underlying assumptions behind common regression methods and utilize diagnostic tools to detect violations of said assumptions

  • Correctly interpret and explain results from regression methods, including interpretation of the coefficients, the p-values, R-squared, and other statistical summaries from regression

  • Apply these methods to real data using the free statistical software R

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
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BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(STAT 240, 301, 302, 312, 324, 371,ECON 310,B M E 325,GEN BUS 306,I SY E 210, or POLI SCI 270) and (STAT 327 or 303, or concurrent enrollment)

STAT 333 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

No instructors recorded for this selection.

Historical instructors & teaching patterns

BI CHENG WU is recorded teaching in Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

ERIK NORDHEIM is recorded teaching in Spring 2009, Spring 2010, Spring 2011, Spring 2012, Fall 2012, Spring 2014, Fall 2015. Recorded history may be incomplete and does not establish a future schedule.

HYUNSEUNG KANG is recorded teaching in Spring 2017, Spring 2019, Fall 2020, Fall 2021, Spring 2022, Fall 2023, Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

KARL ROHE is recorded teaching in Spring 2013, Fall 2013, Fall 2014, Spring 2016, Fall 2016, Fall 2017, Spring 2018, Fall 2019, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

MOHAMED ELKHOULY is recorded teaching in Fall 2020. Recorded history may be incomplete and does not establish a future schedule.

PIXU SHI is recorded teaching in Fall 2018, Fall 2019, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

RICHARD A. JOHNSON is recorded teaching in Spring 2007, Spring 2008. Recorded history may be incomplete and does not establish a future schedule.

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

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 660
DIS 311Classroom Instruction0 / 220
DIS 312Classroom Instruction0 / 220
DIS 313Classroom Instruction0 / 220

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

Past student reviews · Hyunseung Kang, Karl Rohe

Some students found Prof. Kang’s examples relevant, while others found Prof. Rohe’s lectures scattered.

Recent recorded grades — Spring 2025: 3.84 GPA, 94.8% A/AB (n=58 letter grades); Fall 2025: 3.62 GPA, 74.5% A/AB (n=47 letter grades); Spring 2026: 3.31 GPA, 60.7% A/AB (n=56 letter grades).

difficulty & workload

Past student reviews · Karl Rohe

Karl Rohe’s homework was due weekly, long, and unrelated to lectures, while exam questions were vague.

Past student reviews · Erik Nordheim

Erik Nordheim was easy to talk to, though hard to reach outside class.

Topics

  • Linear regression, prediction, feature selection, and interpretation
  • Correlated/dependent features and violation of key assumptions
  • Mixed models and generalized linear models
  • R programming language

Skills

  • Choose and apply regression methods including linear, ANOVA, GLM, and mixed models for prediction and analysis.
  • Identify regression assumptions and use diagnostic tools to detect violations.
  • Interpret regression results such as coefficients, p-values, and R-squared.
  • Apply regression methods to real data using the R programming language.

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

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

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
STAT 333 · Spring 2027

Applied Regression Analysis

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:018455",
    "course_id": "STAT 333",
    "course_uid": "course_ef9c9cd4a7d8002288a4cf7c",
    "term_id": "1274",
    "source_course_id": "018455",
    "source_subject_id": "932",
    "title": "Applied Regression Analysis",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring, Summer"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
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.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "41e0ed2dd5b5a2076a3bb4a02de0e5501e702f8c270c9bc98540cc11ac472686",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}