Spring 2027

Introduction to Industrial Statistics

Introductory industrial statistics course covering probability, descriptive statistics, inference, and regression applied to engineering problems using Microsoft Excel.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Amanda Smith, Hannah Silber

Weekly homework assignments and quizzes formed a substantial part of the workload, often covering current material. A group project required consistent effort, though criteria were clearly defined.

What you’ll be able to do

  • Articulate the importance of statistics in engineering applications

  • Summarize and describe data using descriptive statistics and graphical methods

  • Perform basic statistical analysis on datasets

  • Design simple experiments with data for the purpose of statistical analysis

  • Use Excel to describe, analyze, graph, and interpret data

  • Apply linear and multiple regression techniques

  • Apply experiment and analysis techniques to areas of engineering such as Statistical Process Control

  • Apply basic probability concepts such as random variables, independence, and probability distributions

Source: UW Guide

Grade history

↗
average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 211, 217, or 221) or member of Engineering Guest Students

ISYE 210 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

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

HANNAH SILBER is recorded teaching in Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. 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 / 1970
DIS 301Classroom Instruction0 / 260
DIS 302Classroom Instruction0 / 230
DIS 303Classroom Instruction0 / 260
DIS 304Classroom Instruction0 / 260
DIS 305Classroom Instruction0 / 320
DIS 306Classroom Instruction0 / 320
DIS 307Classroom Instruction0 / 320

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 · Amanda Smith

Students found the course manageable with fair grading and clear lectures. Instructors reviewed exam material thoroughly, and homework served as a significant portion of the final grade, reducing test stress.

Recent recorded grades — Spring 2025: 3.81 GPA, 91.2% A/AB (n=113 letter grades); Fall 2025: 3.79 GPA, 93.1% A/AB (n=130 letter grades); Spring 2026: 3.88 GPA, 95.5% A/AB (n=202 letter grades).

difficulty & workload

Past student reviews · Amanda Smith, Hannah Silber

Weekly homework assignments and quizzes formed a substantial part of the workload, often covering current material. A group project required consistent effort, though criteria were clearly defined.

Past student reviews · Hannah Silber

While instructors were generally supportive, some students noted passive-aggressive comments regarding attendance. Strict discussion attendance policies, such as limited absences allowed even with documentation, were a point of frustration.

Topics

  • Probability, random variables, distributions, descriptive statistics, and point estimation
  • Hypothesis testing, confidence intervals, and design of experiments
  • Regression and correlation analysis
  • Statistical Process Control and engineering applications

Skills

  • Hypothesis testing, confidence intervals, design of experiments, regression and correlation analysis
  • Probability distributions, random variables, descriptive statistics, and point estimation
  • Using Microsoft Excel to describe, analyze, graph, and interpret data
  • Applying statistical methods to engineering problems including Statistical Process Control

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 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

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
ISYE 210 · Spring 2027

Introduction to Industrial Statistics

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": "1274:490:026005",
    "course_id": "ISYE 210",
    "course_uid": "course_75474c11dc2b9f0b6f591be5",
    "term_id": "1274",
    "source_course_id": "026005",
    "source_subject_id": "490",
    "title": "Introduction to Industrial Statistics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "816850edacd6b0811280c16b784fb14322d5e704b7ae29868f9ad817e3df2c82",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}