Useful Links
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
Multiple Linear Regression
Regression Model Framework
Simple vs. Multiple Regression
Model Specification
Variable Types
Dependent Variables
Independent Variables
Continuous Variables
Categorical Variables
Model Assumptions
Linearity
Independence of Errors
Homoscedasticity
Normality of Errors
No Perfect Multicollinearity
Fixed X Assumption
Parameter Estimation
Ordinary Least Squares Method
Matrix Formulation
Normal Equations
Geometric Interpretation
Model Evaluation
Coefficient of Determination
R-squared
Adjusted R-squared
Predicted R-squared
Standard Error of Estimate
Analysis of Variance
ANOVA Table
F-Test for Overall Significance
Sum of Squares Decomposition
Coefficient Interpretation
Unstandardized Coefficients
Standardized Coefficients (Beta)
Partial Correlation Coefficients
Semi-Partial Correlations
Hypothesis Testing
Individual Coefficient Tests
t-Tests
Confidence Intervals
Joint Hypothesis Tests
F-Tests for Subsets
Regression Diagnostics
Residual Analysis
Residual Types
Residual Plots
Normal Probability Plots
Influence Diagnostics
Leverage Values
Cook's Distance
DFBETAS
DFFITS
Multicollinearity Diagnostics
Variance Inflation Factor
Tolerance
Condition Indices
Eigenvalue Analysis
Variable Selection
Forward Selection
Backward Elimination
Stepwise Regression
All Possible Regressions
Information Criteria
AIC
BIC
Mallows' Cp
Model Validation
Cross-Validation
Split-Sample Validation
Bootstrap Methods
Previous
6. Factor Analysis
Go to top
Next
8. Multivariate Analysis of Variance