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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
Data Preparation and Exploration
Data Screening and Quality Assessment
Data Structure Examination
Variable Types and Scales
Data Range and Distribution Checks
Missing Data Analysis
Types of Missingness
Missing Completely at Random (MCAR)
Missing at Random (MAR)
Missing Not at Random (MNAR)
Missing Data Patterns
Imputation Methods
Mean Imputation
Regression Imputation
Multiple Imputation
Maximum Likelihood Methods
Deletion Methods
Listwise Deletion
Pairwise Deletion
Outlier Detection and Treatment
Univariate Outliers
Z-Score Method
Interquartile Range Method
Modified Z-Score
Multivariate Outliers
Mahalanobis Distance
Leverage Values
Cook's Distance
Outlier Treatment Strategies
Deletion
Transformation
Winsorization
Assumption Checking
Normality Assessment
Univariate Normality
Multivariate Normality
Linearity Assessment
Scatterplot Matrices
Partial Regression Plots
Homoscedasticity Assessment
Residual Plots
Levene's Test
Box's M Test
Multicollinearity Assessment
Correlation Matrix Inspection
Variance Inflation Factor
Condition Index
Tolerance Values
Data Transformation
Standardization
Z-Score Standardization
Robust Standardization
Normalization
Min-Max Scaling
Unit Vector Scaling
Variable Transformations
Centering
Polynomial Transformations
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3. The Multivariate Normal Distribution
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5. Principal Component Analysis