Statistics
Multivariate Statistical Analysis
Mean vectors, covariance matrices and the multivariate normal, and the methods built on them, like principal components and discriminant analysis.
- Advanced
- Video lessons
- Free
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About this course
Most real data has more than one variable, and those variables are rarely independent. Multivariate statistics gives you the tools to study them together, working with vectors and matrices instead of one number at a time.
You will lean on linear algebra a lot. If matrices, eigenvalues or quadratic forms feel rusty, the linear algebra refresher goes over them.
Keep the reference sheet open for the notation and the main results.
Good to know first
- Linear algebra with matrices and eigenvalues
- Probability and random variables
- Introductory statistics and hypothesis tests
Main topics
The standard topics of this subject. Use the list to check what you already know.
- Random vectors and covariance matrices
- The multivariate normal distribution
- Inference about mean vectors
- Hotelling’s T² test
- MANOVA
- Principal component analysis
- Factor analysis
- Discriminant analysis and classification
- Canonical correlation analysis
- Cluster analysis