Spring 2027

Introductory Nonparametric Statistics

Introductory course on distribution-free statistical procedures, covering order statistics, ranks, nonparametric tests, permutation methods, kernel density estimation, and computer techniques.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Chunming Zhang

Assignments were comprehensive but rewarding, and exams aligned with taught material without being too difficult. The course covered nonparametric tests and kernel smoothing implementation.

Grade history

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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 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

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

DONG XIA is recorded teaching in Spring 2017. 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

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Past student reviews · Chunming Zhang

Chunming Zhang's lectures were described as dry, and the course did not provide slides or practice exams. Students reported that most of the class skipped lectures.

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

Past student reviews · Chunming Zhang

Assignments were comprehensive but rewarding, and exams aligned with taught material without being too difficult. The course covered nonparametric tests and kernel smoothing implementation.

Past student reviews · Chunming Zhang

The TA was a harsh grader, with many students receiving grades of B or below. Additionally, the professor and TA were not accommodating to a student who missed lectures due to personal circumstances.

Topics

  • Basic tools, counting methods, order statistics, ranks, and empirical distribution functions.
  • Nonparametric hypothesis testing methods including sign, signed rank, Mann-Whitney Wilcoxon, Kolmogorov-Smirnov, and permutation tests.
  • Kernel density estimation and kernel/spline regression estimation.
  • Comparison of nonparametric methods with parametric approaches.

Skills

  • Apply distribution-free tests including sign, signed rank, Mann-Whitney Wilcoxon, Kolmogorov-Smirnov, and permutation methods.
  • Perform kernel density estimation and kernel or spline regression estimation.
  • Utilize order statistics, ranks, and empirical distribution functions as basic tools.
  • Use computer techniques and programs for nonparametric analysis.

Grades

Historical instructor

Spring 2027 · 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 4 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.

5 earlier forecasts · 0.25 GPA average error.

Grades over time

Through Spring 2027

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

1286 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

No offering records for the selected term.

Raw records 
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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.

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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
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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": "1299980dfe94b62b7726af1fb8c6aaf80b2e009b79e2c366a5e4ad5488c018a9",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}