STEM / applied
Multivariate statistics · Undergraduate · Math
Learning objectives from the Multivariate statistics syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Multivariate statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Multivariate distributions
- Random vectors and mean vectors — Random vectors and mean vectors
- Covariance and correlation matrices — Covariance and correlation matrices
- Multivariate normal distribution: properties — Multivariate normal distribution: properties
- Hotelling's T² test — Hotelling's T² test
- Wishart distribution (introduction) — Wishart distribution (introduction)
Dimension reduction and grouping
- Principal component analysis (PCA) — Principal component analysis (PCA)
- Factor analysis (introduction) — Factor analysis (introduction)
- Cluster analysis: hierarchical — Cluster analysis: hierarchical
- K-means — K-means
- Discriminant analysis: LDA and QDA — Discriminant analysis: LDA and QDA
- Canonical correlation analysis (introduction) — Canonical correlation analysis (introduction)
Multivariate inference
- Multivariate analysis of variance (MANOVA) — Multivariate analysis of variance (MANOVA)
- Profile analysis — Profile analysis
- Multiple testing in multivariate settings — Multiple testing in multivariate settings
- Graphical methods: biplots — Graphical methods: biplots
- Scree plots — Scree plots
Learning objectives
Click an objective for study materials.
Multivariate distributions
- Random vectors and mean vectors — Random vectors and mean vectors
- Covariance and correlation matrices — Covariance and correlation matrices
- Multivariate normal distribution: properties — Multivariate normal distribution: properties
- Hotelling's T² test — Hotelling's T² test
- Wishart distribution (introduction) — Wishart distribution (introduction)
Dimension reduction and grouping
- Principal component analysis (PCA) — Principal component analysis (PCA)
- Factor analysis (introduction) — Factor analysis (introduction)
- Cluster analysis: hierarchical — Cluster analysis: hierarchical
- K-means — K-means
- Discriminant analysis: LDA and QDA — Discriminant analysis: LDA and QDA
- Canonical correlation analysis (introduction) — Canonical correlation analysis (introduction)
Multivariate inference
- Multivariate analysis of variance (MANOVA) — Multivariate analysis of variance (MANOVA)
- Profile analysis — Profile analysis
- Multiple testing in multivariate settings — Multiple testing in multivariate settings
- Graphical methods: biplots — Graphical methods: biplots
- Scree plots — Scree plots
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Multivariate distributions
Random vectors and mean vectors
Coming soon - Dimension reduction and grouping
Principal component analysis (PCA)
Coming soon - Multivariate inference
Multivariate analysis of variance (MANOVA)
Coming soon
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$1,162 · Multivariate statistics · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.