Fall 2026

Introductory Nonparametric Statistics

STAT 351 introduces distribution-free statistical methods, covering rank tests, permutation methods, and kernel estimation.

offering recorded3 credits
Recorded instructors · Fall 2026 Joshua Cape3.4/5Kyungjin Sohn

Summary

1 / 6

Historical reviews of Chunming Zhang: Assignments and projects are generally inline with lectures and comprehensive, but students note a lack of practice materials and exams that may not reflect course content.

Grade history

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average GPA
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All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 333, 340, graduate/professional standing, or declared in Statistics VISP

  • take one
STAT 351

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.50/5 from 6 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.

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

Recent recorded grades — Spring 2025: 3.46 GPA, 67.2% A/AB (n=58 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews of Dong Xia: Dong Xia is described as a good lecturer who uses many examples and provides clear notes. Reviewers found his exams fair and homework manageable, characterizing the course as a very good overall experience under his instruction.

DONG XIA is recorded teaching in Spring 2017. Recorded history may be incomplete and does not establish a future schedule.

JOSHUA CAPE is recorded teaching in Spring 2025. 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 Instruction21 / 670
LEC 002Classroom Instruction5 / 50

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

Overall, historical reviews for Dong Xia and Chunming Zhang highlight clear lectures and fair exams, though recent feedback for Zhang emphasizes dry lectures and unaccommodating policies.

Recent recorded grades — Spring 2023: 3.24 GPA, 52.5% A/AB (n=61 letter grades); Spring 2024: 3.27 GPA, 54.8% A/AB (n=62 letter grades); Spring 2025: 3.46 GPA, 67.2% A/AB (n=58 letter grades).

difficulty & workload

Historical reviews of Chunming Zhang: Assignments and projects are generally inline with lectures and comprehensive, but students note a lack of practice materials and exams that may not reflect course content.

Historical reviews of Chunming Zhang, Dong Xia: Students appreciate clear examples and informative material, but recent experiences cite frustration with dry lectures, harsh TA grading, and limited access to lecture slides or recordings.

Topics

  • Order statistics, ranks, and empirical distribution functions.
  • Sign tests, signed rank tests, and Mann-Whitney-Wilcoxon procedures.
  • Kolmogorov-Smirnov tests.
  • Permutation methods.
  • Kernel density estimation and kernel/spline regression estimation.

Skills

  • Implementing statistical procedures using computer software.
  • Comparing nonparametric methods with parametric alternatives.

Grades

Latest available · Spring 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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Grades over time

Through Spring 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2025 · 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

1289 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 351 · Fall 2026

Introductory Nonparametric Statistics

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": "1272:932:018459",
    "course_id": "STAT 351",
    "course_uid": "course_e960d322d17638a03f3d8a4b",
    "term_id": "1272",
    "source_course_id": "018459",
    "source_subject_id": "932",
    "title": "Introductory Nonparametric Statistics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
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": "b9dd5c84121c062d66a28f4d804d604c25f64226441658ed28596feef2112fbc",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}