Applied Multivariate Analysis
STAT 456 teaches multivariate statistical learning methods for analyzing complex datasets, covering dimension reduction, clustering, and classification using R.
Summary
Historical reviews of Wei-Yin Loh: The workload consisted of a series of small homework assignments and one major semester project.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course map(STAT 333 or 340) and (MATH 320, 340, 341, 345, or 375), graduate/professional standing, or declared in Statistics VISP
This is a best-effort interpretation; check the catalog requirements above.
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical reviews of Wei-Yin Loh: Wei-Yin Loh focused on important attributes of multivariate analysis and spent time on all relevant topics. The course structure relied on a single semester project and small homework assignments.
WEI-YIN LOH is recorded teaching in Fall 2009, Fall 2011, Fall 2014, Fall 2016, Fall 2018, Fall 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 2026Schedule 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
No records available.
Student experience
the class
Historical reviews of Wei-Yin Loh: Wei-Yin Loh's STAT 456 focused on a single semester project and small homeworks, emphasizing applied multivariate analysis attributes without unnecessary content.
Recent recorded grades — Spring 2024: 3.50 GPA, 73.0% A/AB (n=37 letter grades); Spring 2025: 3.64 GPA, 81.6% A/AB (n=38 letter grades); Spring 2026: 3.39 GPA, 62.7% A/AB (n=51 letter grades).
difficulty & workload
Historical reviews of Wei-Yin Loh: The workload consisted of a series of small homework assignments and one major semester project.
Historical reviews of Wei-Yin Loh: The professor ensured coverage of all important subject attributes, providing a focused and relevant applied learning experience.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
Grades over time
Through Spring 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
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
[]
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 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": "751c9d8c9e58e68f2af247f7fad68e39b532610d60c38fe30837d4b64209afa7",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}