Fall 2026

Statistics: Measurement in Economics

ECON 310 introduces the analysis of economic data using descriptive statistics and statistical inference techniques like hypothesis testing and estimation.

offering recorded4 credits

Summary

1 / 7

The course is described as hard, with difficult midterms and finals. Workload includes weekly homework via MindTap or Aplia, which some find time-consuming. One review notes that exams can be harder than practice problems.

Grade history

average GPA
letter grades
A
AB
B
BC
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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)

“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

Fall 2026
/5Adjusted rating
/5RMP 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.

Gregory Pac is described as kind, available, and helpful, with many extra credit opportunities and a generous curve. Reviewers appreciate his straightforward teaching and desire for student success, though some find his lectures boring or awkward.

Other reviewers report significant difficulties, citing brutal exams that differ from practice problems and vague slides. Some find the course unhelpful for beginners, with one reviewer calling him strict and unkind, while another notes excessive focus on AI over teaching.

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).

/5Adjusted rating
/5RMP 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.

David Hansen is described as polite and helpful in office hours, though his lectures are frequently called dry, boring, or overly technical. While some students find his explanations clear and effective, others report that the material is difficult to comprehend without significant independent study. Reviewers disagree on his teaching quality, ranging from useless to really kind.

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).

No course-specific feedback yet.

Historical instructors & teaching patterns

Historical reviews of Lorenzo Magnolfi: Lorenzo Magnolfi provides engaging lectures with posted notes and generous grading curves, though exams remain challenging due to tight time limits. Reviewers emphasize that careful preparation is essential to succeed in his class.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction140 / 1541
LAB 302Classroom Instruction21 / 220
LAB 305Classroom Instruction22 / 220
LEC 002Classroom Instruction229 / 2284
LAB 310Classroom Instruction21 / 211
LAB 311Classroom Instruction23 / 231
LAB 312Classroom Instruction20 / 200
LAB 313Classroom Instruction24 / 241
LAB 303Classroom Instruction21 / 221
LAB 306Classroom Instruction20 / 220
LAB 314Classroom Instruction20 / 200
LAB 315Classroom Instruction21 / 200
LAB 301Classroom Instruction20 / 220
LAB 316Classroom Instruction20 / 200
LAB 317Classroom Instruction21 / 201
LAB 307Classroom Instruction22 / 220
LAB 308Classroom Instruction14 / 220
LAB 318Classroom Instruction19 / 200
LAB 319Classroom Instruction20 / 200
LAB 320Classroom Instruction20 / 200

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

Meeting source records

Student experience

the class

Gregory Pac is praised as kind and helpful, with straightforward teaching and extra credit opportunities. David Hansen is viewed as kind and effective at explaining concepts, though his lectures are described as dry and the class as difficult.

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

The course is described as hard, with difficult midterms and finals. Workload includes weekly homework via MindTap or Aplia, which some find time-consuming. One review notes that exams can be harder than practice problems.

Office hours are cited as helpful resources for both instructors. Gregory Pac is appreciated for his availability and kindness, while one review criticizes his focus on AI over content. David Hansen is noted for being willing to help students understand material.

Topics

  • Economic data analysis.
  • Descriptive statistics.
  • Statistical inference.
  • Hypothesis testing and estimation.

Skills

  • Descriptive statistics and statistical inference techniques.
  • Analysis of economic data.
  • Hypothesis testing and estimation.

Grades

Historical instructor

Fall 2026 · 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 · 0.09 GPA average error.

Grades over time

Through Fall 2026

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

1283 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.

Selected offering source records
ECON 310 · Fall 2026

Statistics: Measurement in Economics

Recorded 2026-09-07
Raw records
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    "offering_id": "1272:296:005569",
    "course_id": "ECON 310",
    "course_uid": "course_e104fb600738e5af6d58c4dd",
    "term_id": "1272",
    "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
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
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  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "8de4cd4f190052b73033e66fa4db9bccbb80ce983eec34872a0cebeb75d005dd",
  "requirements_status": "valid",
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