Spring 2027

Introduction to Quality Engineering

Introduction to Quality Engineering covers statistically based quality improvement methods for industrial settings, including design of experiments and robust products.

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

Summary

1 / 6

Past student reviews · Terry Mann

The review noted a low difficulty rating, though it did not specify particular workload demands or assessment details.

What you’ll be able to do

  • Design experiments for various phases of engineering work, including new product design and development, process development, manufacturing process improvement, and health systems design and improvement

  • Analyze the results of experiments conducted at different phases of engineering work

  • Apply data analysis skills in statistical methodologies, graphical displays, and visual and inferential interpretations

  • Work effectively in an experiential project focused on applying appropriate statistical tools and techniques

  • Explain the eight phases of Six Sigma and apply these steps to a wide range of actual situations

  • Demonstrate ability to synthesize applications of statistical methodologies via analysis of current literature and case studies

Source: UW Guide

Grade history

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

Prerequisites

Course map

(I SY E 210,MATH/​STAT 310, 410,STAT 312,B M E 325, or concurrent enrollment), graduate/professional standing, or member of Engineering Guest Students

ISYE 575

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

TERRY MANN is recorded teaching in Fall 2008, Fall 2009, Fall 2010, Fall 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018. 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

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

Meeting source records

No records available.

Student experience

the class

Past student reviews · Terry Mann

A past student described Terry Mann as their favorite teacher and expressed enthusiasm for continuing with his courses.

Recent recorded grades — Spring 2016: 3.71 GPA, 85.5% A/AB (n=55 letter grades); Spring 2017: 3.79 GPA, 92.1% A/AB (n=63 letter grades); Spring 2018: 3.82 GPA, 92.6% A/AB (n=68 letter grades).

difficulty & workload

Past student reviews · Terry Mann

The review noted a low difficulty rating, though it did not specify particular workload demands or assessment details.

Past student reviews · Terry Mann

The student reported a highly positive experience, citing Terry Mann’s teaching quality and their intention to take another class with him.

Topics

  • Observational methods and design of experiments
  • Experimentation to discover influential factors and analyze sources of variation
  • Robust product design

Skills

  • Design experiments and analyze sources of variation using observational methods.
  • Apply the eight phases of Six Sigma to actual situations.

Grades

Latest available · Spring 2018— 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 2018

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2018 · 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

1004 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 
[]
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
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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": "bd4789b7158ea851c23bfabad344a051beb0bdb20c0b0553c6193bc639cd6177",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}