Spring 2027

Statistics: Measurement in Economics

Statistics: Measurement in Economics introduces analysis of economic data using descriptive statistics and statistical inference, including hypothesis testing and estimation for economic research.

offering recorded4 credits
Recorded instructors · Spring 2027 Gregory Pac3.8/5David Hansen3.0/5

Summary

1 / 6

Past student reviews · David Hansen

David Hansen's midterms were very hard, making up 30% of the grade with the option to drop one. The final exam was described as grueling, though homework and Stata assignments were considered easy.

What you’ll be able to do

  • Apply probability theory to model the likelihood of uncertain events.

  • Use software to apply statistical techniques to the analysis of economic data.

  • Interpret tables, graphs and statistics used to summarize data.

  • Estimate the true value of unknown parameters using point and interval estimators.

  • Critically evaluate interpretations of statistical estimates and resulting inferences.

  • Test theories by determining an appropriate test statistic, implementing a formal hypothesis test, and interpreting the outcome.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(ECON 101, 102, or 111) and (MATH 211, 217, or 221)

ECON 310 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.79/5 from 103 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 3.4/5 raw quality · 3.4/5 difficulty · 55 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Gregory Pac

Gregory Pac taught this introductory statistics course with mixed feedback on difficulty; some students found him kind and helpful with generous extra credit opportunities, while others described the material as brutal and exams as significantly harder than practice problems. Students noted that lectures were sometimes vague or boring, requiring active problem-solving and prior statistics experience to succeed comfortably.

Recent recorded grades — Spring 2025: 3.03 GPA, 44.9% A/AB (n=207 letter grades); Fall 2025: 3.11 GPA, 52.2% A/AB (n=247 letter grades); Spring 2026: 3.07 GPA, 46.8% A/AB (n=203 letter grades).

David Hansen

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.78/5 from 73 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.7/5 raw quality · 4.3/5 difficulty · 54 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · David Hansen

David Hansen’s ECON 310 was a hard class with very difficult midterms, but he posted past exams on Canvas and was willing to help in office hours. Students found his lectures dry and technical, often relying on homework and self-study to succeed. While some criticized the grading, others appreciated his clarity and support.

Recent recorded grades — Spring 2025: 3.22 GPA, 45.3% A/AB (n=150 letter grades); Fall 2025: 3.10 GPA, 46.2% A/AB (n=145 letter grades); Spring 2026: 3.15 GPA, 50.4% A/AB (n=117 letter grades).

Historical instructors & teaching patterns

Past student reviews · Jack Porter, Lorenzo Magnolfi

Jack Porter taught with a challenging but fair midterm, while Lorenzo Magnolfi's exams were described as difficult yet supported by engaging lectures and review sessions. Porter's grading breakdown included problem sets, a midterm, and a final. Students noted that strong TAs were crucial for understanding the material, particularly in the latter half of the course.

ANDRES ARADILLAS-LOPEZ is recorded teaching in Fall 2009, Fall 2010, Spring 2012, Spring 2013. Recorded history may be incomplete and does not establish a future schedule.

CHRISTOPHER MCKELVEY is recorded teaching in Fall 2012, Spring 2014, Fall 2014, Fall 2015, Fall 2016, Spring 2017, Spring 2018, Fall 2018, Fall 2021, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

DAVID HANSEN is recorded teaching in Spring 2015, Fall 2015, Spring 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

FRANCESCO DECAROLIS is recorded teaching in Spring 2010, Spring 2011. Recorded history may be incomplete and does not establish a future schedule.

GEOFFREY WALLACE is recorded teaching in Fall 2013. Recorded history may be incomplete and does not establish a future schedule.

GRZEGORZ PAC is recorded teaching in Fall 2019, Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

JACK PORTER is recorded teaching in Fall 2008, Spring 2009, Fall 2011, Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

JAMES R WALKER is recorded teaching in Fall 2006. Recorded history may be incomplete and does not establish a future schedule.

WILLIAM SANDHOLM is recorded teaching in Fall 2017, Spring 2019. 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
LEC 001Classroom Instruction0 / 1440
LAB 306Classroom Instruction0 / 240
LAB 301Classroom Instruction0 / 240
LAB 302Classroom Instruction0 / 240
LAB 304Classroom Instruction0 / 240
LAB 322Classroom Instruction0 / 240
LAB 303Classroom Instruction0 / 240
LAB 321Classroom Instruction0 / 240
LAB 323Classroom Instruction0 / 240
LEC 002Classroom Instruction0 / 2160
LAB 305Classroom Instruction0 / 240
LAB 324Classroom Instruction0 / 240
LAB 325Classroom Instruction0 / 240
LAB 326Classroom Instruction0 / 240
LAB 327Classroom Instruction0 / 240
LAB 328Classroom Instruction0 / 240
LAB 329Classroom Instruction0 / 240

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Gregory Pac, David Hansen

Gregory Pac described the course as a 'choose your adventure' where effort determined success, while David Hansen's class was described as hard but manageable with enough work put in.

Recent recorded grades — Spring 2025: 3.11 GPA, 45.1% A/AB (n=357 letter grades); Fall 2025: 3.11 GPA, 50.0% A/AB (n=392 letter grades); Spring 2026: 3.10 GPA, 48.1% A/AB (n=320 letter grades).

difficulty & workload

Past student reviews · David Hansen

David Hansen's midterms were very hard, making up 30% of the grade with the option to drop one. The final exam was described as grueling, though homework and Stata assignments were considered easy.

Past student reviews · Gregory Pac

Gregory Pac was seen as highly available and generous with extra credit opportunities. One student noted lectures make sense only if you're already caught up, emphasizing the need to do problems.

Topics

  • Descriptive statistics and statistical inference.
  • Application of statistical methods in economic research.
  • Probability theory for modeling uncertain events.

Skills

  • Apply statistical techniques using software to analyze economic data.
  • Implement formal hypothesis tests and interpret outcomes.
  • Estimate unknown parameters using point and interval estimators.
  • Interpret tables, graphs, and summary statistics.

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.10 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · 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

1280 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
ECON 310 · Spring 2027

Statistics: Measurement in Economics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:296:005569",
    "course_id": "ECON 310",
    "course_uid": "course_e104fb600738e5af6d58c4dd",
    "term_id": "1274",
    "source_course_id": "005569",
    "source_subject_id": "296",
    "title": "Statistics: Measurement in Economics",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Fall, Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
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
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2Download part 3
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2ee96a325c5e3d0c94573dfa06be09f663a7f56ee9d5957cbeb7ae403fcdcbe4",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}