Spring 2027

Introduction to Statistics for Science and Engineering

Introduction to Statistics for Science and Engineering covers descriptive stats, probability, hypothesis testing, linear regression, ANOVA, and experimental design using R.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Michael Culbertson

Despite considerable use of R in the course, students were taught only the bare minimum required for assignments.

What you’ll be able to do

  • Articulate the basics of probability and statistics.

  • Make numeric and graphical summaries of simple data

  • Produce appropriate statistical analyses of simple data sets

  • Design simple experiments with data that will suit basic statistical analysis

  • Use R for statistical computations and graphs

  • Learn additional statistical methods

Source: UW Guide

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 211, 217, or 221. Not open to students with credit forSTAT 371.

STAT 324 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

BO YANG is recorded teaching in Fall 2015, Spring 2016. 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.

COLIN LONGHURST is recorded teaching in Fall 2015, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

DEBRAJ DAS is recorded teaching in Fall 2017, Spring 2018. Recorded history may be incomplete and does not establish a future schedule.

DONG XIA is recorded teaching in Fall 2016, Spring 2017. Recorded history may be incomplete and does not establish a future schedule.

DUZHE WANG is recorded teaching in Spring 2019, Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

JARED BROWN is recorded teaching in Fall 2016, Fall 2017. Recorded history may be incomplete and does not establish a future schedule.

MICHAEL CULBERTSON is recorded teaching in Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

MINJING TAO is recorded teaching in Spring 2012, Fall 2012, Spring 2013. Recorded history may be incomplete and does not establish a future schedule.

RAHUL MAHAJAN is recorded teaching in Fall 2019. 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 / 2000
LEC 002Classroom Instruction0 / 2000

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 · Brian Powers

Students reported that lectures often felt excessively long, with content that could have been covered in a fraction of the time.

Recent recorded grades — Spring 2025: 2.95 GPA, 37.3% A/AB (n=383 letter grades); Fall 2025: 2.95 GPA, 36.5% A/AB (n=370 letter grades); Spring 2026: 2.99 GPA, 37.2% A/AB (n=336 letter grades).

difficulty & workload

Past student reviews · Michael Culbertson

Despite considerable use of R in the course, students were taught only the bare minimum required for assignments.

Past student reviews · Bi Cheng Wu

One student found the final exam extremely hard and noted that the professor did not explain final grades or allow arguments for them.

Topics

  • Descriptive statistics, probability, distributions, and random variables.
  • Hypothesis tests and confidence intervals.
  • Linear regression and model checking.
  • Analysis of variance and experimental design.
  • Use of R programming language.

Skills

  • Descriptive statistics, probability concepts, distributions, and random variables.
  • Hypothesis testing and confidence intervals for one- and two-sample problems.
  • Linear regression, model checking, and statistical inference.
  • Analysis of variance (ANOVA) and basic experimental design.
  • Statistical computation and graphing using R.

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 · 0.17 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 324 · Spring 2027

Introduction to Statistics for Science and Engineering

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": "1274:932:021159",
    "course_id": "STAT 324",
    "course_uid": "course_06abb97e9793bf858754daa3",
    "term_id": "1274",
    "source_course_id": "021159",
    "source_subject_id": "932",
    "title": "Introduction to Statistics for Science and Engineering",
    "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
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": "8696e3725d39af6600987d57614fa9301c8d0e27ff5f51cd9dd10ffba4b0febe",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}