Fall 2026

Applied Regression Analysis

Applied Regression Analysis covers linear regression for prediction and interpretation, including feature selection, assumption checking, and extensions like mixed and generalized linear models using R.

offering recorded3 credits
Recorded instructors · Fall 2026 Karl Rohe2.8/5Jingyang Lyu

Summary

1 / 7

Weekly homework is described as long and unrelated to lectures, while the final project is considered very important and strictly graded. Students report dealing with shifting deadlines and vague test questions that rely on the professor's interpretation.

Grade history

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average GPA
letter grades
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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, orI SY E 210) 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

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.45/5 from 58 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.1/5 raw quality · 2.9/5 difficulty · 28 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Karl Rohe’s lectures are often criticized as disorganized, scattered, and lacking focus, with vague exams and unclear project expectations. Students find the course stressful due to shifting deadlines and insufficient theoretical explanation, though some note he is personally nice and offers practical advice.

Other students find Rohe engaging and caring, particularly for data science interests. They highlight his openness to extra help, fair deadlines, and the value of the course project, recommending the class despite minor organizational issues on Canvas.

Recent recorded grades — Fall 2019: 3.04 GPA, 48.7% A/AB (n=39 letter grades); Spring 2024: 3.57 GPA, 81.5% A/AB (n=27 letter grades); Spring 2025: 3.84 GPA, 94.8% A/AB (n=58 letter grades).

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction63 / 723
DIS 312Classroom Instruction22 / 241
DIS 313Classroom Instruction21 / 242
DIS 311Classroom Instruction20 / 240

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

Karl Rohe’s teaching style is highly polarizing, with students describing lectures as either useless and disorganized or engaging and practical. While some find the course stressful due to vague expectations, others appreciate his focus on statistical learning and personal care.

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

Weekly homework is described as long and unrelated to lectures, while the final project is considered very important and strictly graded. Students report dealing with shifting deadlines and vague test questions that rely on the professor's interpretation.

Students suggest asking questions to steer lectures toward actual teaching and spending significant time on the project. While some find the course disorganized and hard to reach outside of class, others value his openness to extra help and practical advice.

Topics

  • Linear regression
  • Feature selection
  • Correlated and dependent features
  • Mixed models
  • Generalized linear models
  • R programming language

Skills

  • Performing prediction, feature selection, and interpretation using linear regression.
  • Analyzing the impact of correlated features and assessing regression assumptions.
  • Applying mixed models and generalized linear models.
  • Implementing statistical methods using the R programming language.

Grades

Historical instructor

Fall 2026 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.28 GPA average error.

Grades over time

Through Fall 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

1283 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 · Fall 2026

Applied Regression Analysis

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:932:018455",
    "course_id": "STAT 333",
    "course_uid": "course_ef9c9cd4a7d8002288a4cf7c",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "3c0e8fc464ed6f79a5a85b281f5c1aeddc4ea1617174c9d9c1f23b40df9bb08d",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}