UsefulLinks
Statistics
Multivariate Analysis
1. Introduction to Multivariate Analysis
2. Foundations in Matrix Algebra and Random Vectors
3. The Multivariate Normal Distribution
4. Data Preparation and Exploration
5. Principal Component Analysis
6. Factor Analysis
7. Multiple Linear Regression
8. Multivariate Analysis of Variance
9. Discriminant Analysis
10. Logistic Regression
11. Cluster Analysis
12. Canonical Correlation Analysis
13. Multidimensional Scaling
14. Advanced Multivariate Methods
12.
Canonical Correlation Analysis
12.1.
Canonical Correlation Objectives
12.1.1.
Relationship Between Variable Sets
12.1.2.
Dimension Reduction
12.1.3.
Redundancy Analysis
12.2.
Canonical Variates
12.2.1.
Linear Combinations
12.2.2.
Canonical Weights
12.2.3.
Canonical Scores
12.3.
Mathematical Foundation
12.3.1.
Eigenvalue Problem
12.3.2.
Canonical Correlations
12.3.3.
Number of Canonical Functions
12.4.
Assumptions
12.4.1.
Multivariate Normality
12.4.2.
Linear Relationships
12.4.3.
Homoscedasticity
12.4.4.
Adequate Sample Size
12.5.
Canonical Correlation Computation
12.5.1.
Matrix Formulation
12.5.2.
Eigenvalue Extraction
12.5.3.
Canonical Weight Calculation
12.6.
Interpretation of Results
12.6.1.
Canonical Loadings
12.6.2.
Canonical Cross-Loadings
12.6.3.
Canonical Scores
12.6.4.
Structure Coefficients
12.7.
Significance Testing
12.7.1.
Wilks' Lambda
12.7.2.
F-Approximations
12.7.3.
Individual Canonical Correlations
12.8.
Redundancy Analysis
12.8.1.
Variance Explained
12.8.2.
Redundancy Indices
12.8.3.
Practical Significance
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13. Multidimensional Scaling