Applied Econometrics of Resource and Energy Demand
Applied econometrics for estimating and forecasting resource and energy demand using advanced statistical models.
Summary
Recent recorded grades — Spring 2019: 3.57 GPA, 71.4% A/AB (n=28 letter grades); Spring 2020: 3.63 GPA, 78.3% A/AB (n=23 letter grades); Spring 2022: 3.45 GPA, 70.0% A/AB (n=10 letter grades).
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course mapA A E 636 and declared in Agricultural Applied Economics: Resource and Energy Demand Analysis
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- declared in Agricultural Applied Economics: Resource and Energy Demand Analysis
- AAE 636
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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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
Recent recorded grades — Spring 2019: 3.57 GPA, 71.4% A/AB (n=28 letter grades); Spring 2020: 3.63 GPA, 78.3% A/AB (n=23 letter grades); Spring 2022: 3.45 GPA, 70.0% A/AB (n=10 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
Skills
Grades
Latest available · Spring 2022— not enough history to project Fall 2026.
Grade distribution · % of letter grades
Grades over time
Through Spring 2022
More grade details Grade mix, volume & source data
Not enough comparable courses for Fall 2026 in UW–Madison.
Sources & history
Catalog & offerings
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Catalog observation history
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Raw records
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Student reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "d61f65566ed2c586def836b8449e0649f654e0d64c26153202481d2f55fb7395",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}