UsefulLinks
Statistics
Design of Experiments
1. Introduction to Design of Experiments
2. Fundamental Principles of Experimental Design
3. Planning and Conducting Experiments
4. Simple Comparative Experiments
5. Analysis of Variance (ANOVA)
6. Factorial Designs
7. Fractional Factorial Designs
8. Response Surface Methodology
9. Robust Design and Taguchi Methods
10. Advanced and Specialized Designs
11. Statistical Analysis and Model Building
12. Software Tools and Implementation
13. Quality Control and Best Practices
14. Applications and Case Studies
5.
Analysis of Variance (ANOVA)
5.1.
One-Way ANOVA
5.1.1.
ANOVA Model and Assumptions
5.1.1.1.
Normality Assumption
5.1.1.2.
Homogeneity of Variance
5.1.1.3.
Independence Assumption
5.1.2.
Sum of Squares Decomposition
5.1.2.1.
Total Sum of Squares
5.1.2.2.
Between-Groups Sum of Squares
5.1.2.3.
Within-Groups Sum of Squares
5.1.3.
F-Test and F-Distribution
5.1.4.
ANOVA Table Construction
5.1.5.
Effect Size Measures
5.1.5.1.
Eta-squared
5.1.5.2.
Omega-squared
5.1.6.
Model Adequacy Checking
5.1.6.1.
Residual Analysis
5.1.6.2.
Normality Tests
5.1.6.3.
Homogeneity Tests
5.1.6.4.
Outlier Detection
5.1.7.
Multiple Comparisons
5.1.7.1.
Family-wise Error Rate
5.1.7.2.
Tukey's HSD Test
5.1.7.3.
Fisher's LSD
5.1.7.4.
Dunnett's Test
5.1.7.5.
Bonferroni Correction
5.1.7.6.
Scheffe's Method
5.1.8.
Non-parametric Alternatives
5.1.8.1.
Kruskal-Wallis Test
5.1.8.2.
Friedman Test
5.2.
Two-Way ANOVA
5.2.1.
Model Structure
5.2.1.1.
Main Effects
5.2.1.2.
Interaction Effects
5.2.1.3.
Additive vs. Non-additive Models
5.2.2.
Balanced vs. Unbalanced Designs
5.2.3.
Sum of Squares Decomposition
5.2.4.
Interaction Interpretation
5.2.5.
Simple Effects Analysis
5.2.6.
Model Adequacy Assessment
5.3.
Higher-Order ANOVA
5.3.1.
Three-Way ANOVA
5.3.2.
Multi-factor Interactions
5.3.3.
Hierarchical Models
Previous
4. Simple Comparative Experiments
Go to top
Next
6. Factorial Designs