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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
Principal Component Analysis
Conceptual Foundation
Objectives of PCA
Dimensionality Reduction
Data Visualization
Noise Reduction
Geometric Interpretation
Variance Maximization Principle
Mathematical Derivation
Covariance Matrix Approach
Correlation Matrix Approach
Eigenvalue Problem Formulation
Lagrange Multiplier Method
PCA Implementation
Data Preparation
Centering
Standardization Decision
Covariance vs. Correlation Matrix Choice
Eigenvalue Decomposition
Principal Component Extraction
Component Score Calculation
Determining Number of Components
Kaiser's Rule (Eigenvalue > 1)
Scree Plot Analysis
Cumulative Variance Explained
Parallel Analysis
Cross-Validation Methods
Interpretation of Results
Component Loadings
Loading Interpretation
Loading Plots
Communalities
Component Scores
Score Calculation
Score Plots
Biplot Construction
Component Rotation
Orthogonal Rotations
Varimax Rotation
Quartimax Rotation
Equamax Rotation
Oblique Rotations
Promax Rotation
Direct Oblimin
Rotation Interpretation
PCA Diagnostics and Validation
Adequacy Measures
Stability Assessment
Cross-Validation
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4. Data Preparation and Exploration
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6. Factor Analysis