Multivariate statistics
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing calculator
Choose materials, tutoring, or both — or book a single session as needed. Customize your plan on the subscribe page.
Billed in 15-minute increments (15-minute minimum, up to 4 hours). No subscription required.
$60.00 · 60 min · Undergraduate · Online ($60/hr)
Book through intake or schedule a session.
Topics typically covered
Standard syllabus
Multivariate distributions
- Random vectors and mean vectors
- Covariance and correlation matrices
- Multivariate normal distribution: properties
- Hotelling's T² test
- Wishart distribution (introduction)
Dimension reduction and grouping
- Principal component analysis (PCA)
- Factor analysis (introduction)
- Cluster analysis: hierarchical and k-means
- Discriminant analysis: LDA and QDA
- Canonical correlation analysis (introduction)
Multivariate inference
- Multivariate analysis of variance (MANOVA)
- Profile analysis
- Multiple testing in multivariate settings
- Graphical methods: biplots and scree plots
STEM / applied
Applied multivariate workflow
- PCA and clustering in R or Python
- Visualization of high-dimensional data
- Feature scaling and preprocessing
- Case studies in marketing, genomics, and image data
- Cross-validation for cluster stability
- Interpretation of components in applied domains
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Typical second-year statistics course for majors. Applied sections focus on interpretation and software; standard sections cover classical multivariate distribution theory at an accessible level.