Regression and Time Series for Actuaries
Regression and Time Series for Actuaries covers multiple regression and time series analysis with a business focus, training students to critically consume related reports.
Summary
Past student reviews · Peng Shi
The content was described as quite difficult and theoretical, with much of it being challenging to grasp initially.
What you’ll be able to do
Complete hands-on tutorials and exercises using linear regression and time series models relevant for actuarial practice.
Utilize statistical software to estimate model parameters for regression models in business applications.
Explain the importance of checking model assumptions and conducting residual analyses in business applications.
Explain the impact of choices of explanatory variables and incorporate these variables in the model.
Integrate regression concepts to other actuarial science courses including loss models, actuarial mathematics, and use of simulation.
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(ACT SCI 640,GEN BUS 656,STAT 333, or 340), or declared in undergraduate Business Exchange program
- take one
- declared in undergraduate Business Exchange program
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- Any of
- declared in undergraduate Business Exchange program
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
PENG SHI is recorded teaching in Fall 2013, Spring 2014, Fall 2014, Fall 2015, Fall 2017, Spring 2018. 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 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Past student reviews · Peng Shi
The course covered theoretical material that was quite difficult, but Professor Shi's teaching style made the experience manageable and engaging.
Recent recorded grades — Fall 2022: 3.15 GPA, 38.5% A/AB (n=26 letter grades); Spring 2024: 3.23 GPA, 42.9% A/AB (n=28 letter grades); Spring 2025: 3.58 GPA, 83.3% A/AB (n=12 letter grades).
difficulty & workload
Past student reviews · Peng Shi
The content was described as quite difficult and theoretical, with much of it being challenging to grasp initially.
Past student reviews · Peng Shi
Students found Professor Shi to be likable, humorous, and down-to-earth, and noted that he made the exams manageable.
Topics
- Multiple regression and time series analysis
- Integration of regression with loss models, actuarial mathematics, and simulation
Skills
- Estimating regression model parameters using statistical software
- Checking model assumptions and conducting residual analyses
- Selecting and incorporating explanatory variables into regression models
Grades
Latest available · Spring 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
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
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "ae60f276aae58aab11bf84edcc52b0e3af1f31ed12bc04e87e81920d24e9efd0",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}