Spring 2027

Applied Multivariate Analysis

Applied Multivariate Analysis covers dimension reduction, clustering, and classification using R, emphasizing conceptual underpinnings and core models for complex datasets.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Wei-Yin Loh

Workload consisted of a one-semester project and a series of small homework assignments, rated as moderate difficulty.

What you’ll be able to do

  • Describe the purpose and basic concepts of multivariate analysis, distinguishing it from other common forms of statistical analysis such as regression analysis.

  • Define fundamental concepts of multivariate analysis, such as covariance and correlation, and explain their meaning.

  • Explain common methods of multivariate analysis such as principal components analysis, factor analysis, multidimensional scaling, and clustering; this includes identifying situations in which these methods would be useful, describing how they are performed, and explaining how to interpret their outputs.

  • Use statistical software such as R to create meaningful visualizations of multivariate data, and to carry out multivariate analyses.

  • Properly interpret, contextualize, and communicate the results of an applied multivariate analysis.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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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

STAT 456

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 750
LEC 002Classroom Instruction0 / 200

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 · Wei-Yin Loh

The course focused on an applied approach to multivariate analysis, with a semester project and small homework assignments forming the core of the experience.

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

Past student reviews · Wei-Yin Loh

Workload consisted of a one-semester project and a series of small homework assignments, rated as moderate difficulty.

Past student reviews · Wei-Yin Loh

Professor Loh spent time covering all important aspects of the subject while avoiding irrelevant material, which students found helpful.

Topics

  • Dimension reduction including PCA, MDS, and factor analysis
  • Clustering methods including k-means, hierarchical, density-based, and model-based
  • Classification techniques including discriminant analysis, trees, and ensembles
  • Fundamental concepts of covariance and correlation

Skills

  • Using R for multivariate data visualization and analysis
  • Applying dimension reduction, clustering, and classification techniques
  • Defining and explaining fundamental multivariate concepts like covariance and correlation

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

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

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
STAT 456 · Spring 2027

Applied Multivariate Analysis

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": "1274:932:002975",
    "course_id": "STAT 456",
    "course_uid": "course_1ea543c8a9e3b7f76f3a2d83",
    "term_id": "1274",
    "source_course_id": "002975",
    "source_subject_id": "932",
    "title": "Applied Multivariate Analysis",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f068d270c88fdac40e302cff6597da634859b7fca7130f2855451678dcf4aca5",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}