Spring 2027

Applied Categorical Data Analysis

Applied Categorical Data Analysis covers contingency tables, association measures, and logistic/Poisson regression using R to analyze categorical response data.

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

Summary

1 / 7

Past student reviews · Derek Bean

Recent students reported a heavy homework burden, including five large assignments weekly with multiple sub-questions. One reviewer noted that assignments often contained two entire large textbook questions, making the workload demanding.

What you’ll be able to do

  • Construct and interpret contingency tables for the purposes of summarizing, displaying, exploring, and analyzing data classified by one or more categorical variables.

  • Calculate and interpret summary measures of association between categorical variables, such as odds ratios.

  • Perform inference for parameters of interest arising from models of categorical data by using point estimators, confidence intervals, and hypothesis tests, and interpreting their results.

  • Fit logistic and Poisson regression models where appropriate; assess the goodness of the model fits; perform model selection; and interpret/make inferences from well-fitting models. This includes the important subcases of fitting logistic and loglinear models to contingency tables.

  • Utilize a statistical computing package (such as R) to carry out analyses of categorical data.

Source: UW Guide

Grade history

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

Prerequisites

Course map

STAT 333, 340, graduate/professional standing, or declared in Statistics VISP

  • take one
STAT 421

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

DEREK BEAN is recorded teaching in Fall 2018, Fall 2019, Spring 2020, Fall 2021, Fall 2022, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

KAM-WAH TSUI is recorded teaching in Fall 2015, Fall 2016. Recorded history may be incomplete and does not establish a future schedule.

WEI-YIN LOH is recorded teaching in Fall 2007, Fall 2008, Fall 2010, Fall 2012, Fall 2014, Fall 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

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Student experience

the class

Past student reviews · Derek Bean

Students found the course content useful for learning R and statistics. Derek Bean's recent sections were described as having good lectures and useful material, though the homework load was significant.

Recent recorded grades — Fall 2022: 3.41 GPA, 71.4% A/AB (n=63 letter grades); Fall 2024: 3.51 GPA, 84.1% A/AB (n=82 letter grades); Fall 2025: 3.31 GPA, 58.9% A/AB (n=73 letter grades).

difficulty & workload

Past student reviews · Derek Bean

Recent students reported a heavy homework burden, including five large assignments weekly with multiple sub-questions. One reviewer noted that assignments often contained two entire large textbook questions, making the workload demanding.

Past student reviews · Derek Bean

Assessments included two take-home exams without time limits, which one student found stress-free. Another noted that lecture notes were available on Canvas and attendance did not matter, though the relentless homework was a major part of the experience.

Topics

  • Multidimensional contingency tables
  • Measures of association between categorical variables
  • Logistic and Poisson regression modeling

Skills

  • Constructing and interpreting contingency tables for categorical data
  • Fitting, assessing, and interpreting logistic and Poisson regression models
  • Using R for statistical analysis of categorical data

Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 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

1316 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.

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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": "eeed24ed96cf52a5cdee33e1b663955176083519c41f3a0794de785d42e41fb7",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}