Fall 2026

Survey and Sample Design in Applied Economics

Teaches survey and sample design in applied economics, covering sampling methods, survey implementation, and econometric modeling of choices.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 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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Prerequisites

Course map

Declared in the Resource and Energy Demand Analysis program

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

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Declared in the Resource and Energy Demand Analysis program

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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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

No workload feedback recorded.

Topics

  • Target population identification and sampling methods
  • Survey collection implementation
  • Respondent choice surveys and experimental design

Skills

  • Generation and use of survey data
  • Econometric modeling of respondent choices

Grades

Latest available · Fall 2019— not enough history to project Fall 2026.

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

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Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "2df0881a83ab05f8c8113df2350a26b5f8e9582e94f7c515454f3d4944200320",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}