Spring 2027

Introduction to Theory and Methods of Mathematical Statistics I

Introduction to mathematical statistics covering probability, discrete distributions, acceptance sampling, normal distribution, central limit theorem, and estimation methods.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 5

Past student reviews · Amy Beyler, Derek Bean

Workload was generally manageable, with some students finding homework tougher than exams. Exams were sometimes described as difficult or tricky, but often aligned with practice materials or were curved.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 234, 376, or concurrent enrollment or graduate/professsional standing. Not open to students with credit forSTAT/​MATH 309 or STAT/​MATH 431

STAT 311 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

AMY BEYLER is recorded teaching in Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

BRIAN POWERS is recorded teaching in Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

DEREK BEAN is recorded teaching in Fall 2018, Spring 2019, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

GONZALO CONTADOR is recorded teaching in Fall 2016, Spring 2017, Spring 2018, Fall 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule.

ISMOR FISCHER is recorded teaching in Fall 2015, Spring 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020. Recorded history may be incomplete and does not establish a future schedule.

JOSEP GINEBRA is recorded teaching in Fall 2010, Spring 2011. Recorded history may be incomplete and does not establish a future schedule.

KARL ROHE is recorded teaching in Fall 2011, Spring 2012, Fall 2012, Spring 2013, Spring 2014. Recorded history may be incomplete and does not establish a future schedule.

NIMAL WICKREMASINGHE is recorded teaching in Spring 2024, Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

PANDUAN AN is recorded teaching in Fall 2021, Spring 2022, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

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

YAOGUO XIE is recorded teaching in Fall 2014, Spring 2015, Fall 2015. Recorded history may be incomplete and does not establish a future schedule.

YAZHEN WANG is recorded teaching in Spring 2010. 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
LEC 002Classroom Instruction0 / 660
LEC 003Classroom Instruction0 / 660

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 · Derek Bean, Nimal Wickremasinghe

Past students described instructors who broke down concepts clearly, though some found lectures boring. Feedback varied widely on accessibility, with some professors being very helpful in office hours while others were less responsive.

Recent recorded grades — Spring 2025: 3.21 GPA, 49.0% A/AB (n=204 letter grades); Fall 2025: 3.18 GPA, 44.1% A/AB (n=195 letter grades); Spring 2026: 2.92 GPA, 37.4% A/AB (n=187 letter grades).

difficulty & workload

Past student reviews · Amy Beyler, Derek Bean

Workload was generally manageable, with some students finding homework tougher than exams. Exams were sometimes described as difficult or tricky, but often aligned with practice materials or were curved.

Past student reviews · Josep Ginebra, Derek Bean

Students appreciated instructors who explained details thoroughly and were patient during office hours. However, some noted that not all lecture notes or recordings were posted online, requiring attendance.

Topics

  • Probability theory and discrete distributions
  • Acceptance sampling and population characteristics
  • Normal distribution, sampling, and central limit theorem
  • Point and interval estimation

Skills

  • Understanding elements of probability and important discrete distributions.
  • Applying acceptance sampling methods and analyzing sample and population characteristics.
  • Working with the normal distribution, sampling from normal populations, and the central limit theorem.
  • Performing point and interval estimation.

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 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 · 0.26 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 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 311 · Spring 2027

Introduction to Theory and Methods of Mathematical Statistics I

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": "1274:932:018443",
    "course_id": "STAT 311",
    "course_uid": "course_1b76508145789f8fbc49d1fa",
    "term_id": "1274",
    "source_course_id": "018443",
    "source_subject_id": "932",
    "title": "Introduction to Theory and Methods of Mathematical Statistics I",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
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 2026-10-14

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": "10c96c58c453ace800ba2171b62293bc498cb3ddd7cbd11920be629b6ed5e353",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}