Spring 2027

Survey and Sample Design in Applied Economics

Teaches generation and use of survey data. Topics include identification of target population, random, stratified, cluster sampling, power analysis, survey collection implementation, retrospective and prospective surveys of respondent choice, experimental choice in survey design, and econometric modeling of respondent choices.

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

Summary

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Recent recorded grades — Fall 2017: 3.50 GPA, 70.0% A/AB (n=20 letter grades); Fall 2018: 3.62 GPA, 80.0% A/AB (n=20 letter grades); Fall 2019: 3.57 GPA, 78.6% A/AB (n=14 letter grades).

Grade history

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average GPA
letter grades
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AB
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Prerequisites

Course map

Declared in the Resource and Energy Demand Analysis program

  • Declared in the Resource and Energy Demand Analysis program
AAE 777

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  • Declared in the Resource and Energy Demand Analysis program

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2017: 3.50 GPA, 70.0% A/AB (n=20 letter grades); Fall 2018: 3.62 GPA, 80.0% A/AB (n=20 letter grades); Fall 2019: 3.57 GPA, 78.6% A/AB (n=14 letter grades).

difficulty & workload

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Grades

Latest available · Fall 2019— 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 2019

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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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

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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": "85550f4bb543e21f1e4d64e8ea55ebd9e3db6b15a77c25012baf5453aa1f967f",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}