Spring 2027

Economic Decision Analysis

An applied managerial course in microeconomic theory focused on quantitative analysis using spreadsheets, executive summaries, and applications to natural resources and agricultural markets.

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

Summary

1 / 8

Past student reviews · Brian Gould

Homework was heavily Excel-based, with students reporting at least six hours per week of work. Tests were described as very difficult and made up a large portion of the final grade.

What you’ll be able to do

  • Manipulate, organize, and visualize quantitative economic data using computer software.

  • Conduct statistical analyses and estimate basic linear regression models of economic data.

  • Correctly report and interpret results from statistical analyses in the context of informing economic decisions.

  • Set up and solve linear and non-linear programming problems that inform economic decision-making using computer software.

  • Integrate uncertainty into the analysis of economic decisions and articulate how uncertainty influences economic behavior.

  • Effectively communicate verbally, visually, and in writing the process and results of economic decision analyses.

Source: UW Guide 1 UW Guide 2

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 301, 371,ECON 310,SOC/​C&E SOC 360,PSYCH 210, or (GEN BUS 306 and 307)

AAE/ECON 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

BRENT HUETH is recorded teaching in Fall 2018, Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

BRIAN GOULD is recorded teaching in Spring 2014, Spring 2015, Spring 2016, Fall 2016, Fall 2017. Recorded history may be incomplete and does not establish a future schedule.

THOMAS L. COX is recorded teaching in Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013. 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
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Student experience

the class

Past student reviews · Thomas Cox, Brian Gould

Students described this as a rigorous, practical course focused on agricultural markets and Excel-based analysis. It demands consistent effort and preparation to succeed.

Recent recorded grades — Fall 2023: 3.05 GPA, 47.6% A/AB (n=63 letter grades); Fall 2024: 3.40 GPA, 70.6% A/AB (n=51 letter grades); Fall 2025: 3.50 GPA, 79.5% A/AB (n=39 letter grades).

difficulty & workload

Past student reviews · Brian Gould

Homework was heavily Excel-based, with students reporting at least six hours per week of work. Tests were described as very difficult and made up a large portion of the final grade.

Past student reviews · Thomas Cox

One student found the teaching style thought-provoking and the lessons long-lasting. Another noted the instructor was initially intimidating but took time to help students who engaged with the material.

Topics

  • Microeconomic theory
  • Natural resources and agricultural markets
  • Linear regression models
  • Linear and non-linear programming

Skills

  • Manipulate, organize, and visualize quantitative economic data using computer software.
  • Conduct statistical analyses and estimate basic linear regression models of economic data.
  • Set up and solve linear and non-linear programming problems that inform economic decision-making using computer software.
  • Write executive summaries of applied economic analyses.

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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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": "37167533c5cab7795baf4534c92447607f005e223e22d335de7409c1bd51700b",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}