UsefulLinks
Statistics
Statistics with R
1. Introduction to R and Statistical Computing
2. R Fundamentals and Basic Operations
3. R Package System
4. R Data Structures
5. Data Import and Export
6. Data Cleaning and Preprocessing
7. Data Manipulation with dplyr
8. Descriptive Statistics
9. Data Visualization Fundamentals
10. Advanced Data Visualization with ggplot2
11. Probability Theory
12. Probability Distributions
13. Sampling and Sampling Distributions
14. Statistical Inference Foundations
15. Hypothesis Testing Framework
16. One-Sample Tests
17. Two-Sample Tests
18. Chi-squared Tests
19. Analysis of Variance (ANOVA)
20. Correlation Analysis
21. Simple Linear Regression
22. Regression Diagnostics
23. Multiple Linear Regression
24. Generalized Linear Models
25. Nonparametric Statistics
26. Introduction to Time Series Analysis
27. Introduction to Machine Learning
28. Reproducible Research
29. Statistical Computing Best Practices
19.
Analysis of Variance (ANOVA)
19.1.
ANOVA Concepts
19.1.1.
Purpose of ANOVA
19.1.2.
Between-group vs Within-group Variation
19.1.3.
F-statistic
19.1.4.
ANOVA Table
19.2.
One-Way ANOVA
19.2.1.
Assumptions
19.2.1.1.
Independence
19.2.1.2.
Normality
19.2.1.3.
Homogeneity of Variance
19.2.2.
Hypothesis Testing
19.2.3.
Implementation in R
19.2.4.
Interpretation of Results
19.3.
ANOVA Diagnostics
19.3.1.
Residual Analysis
19.3.2.
Normality Tests
19.3.3.
Homogeneity Tests
19.3.4.
Outlier Detection
19.4.
Post-Hoc Tests
19.4.1.
Multiple Comparisons Problem
19.4.2.
Tukey's HSD
19.4.3.
Bonferroni Correction
19.4.4.
Scheffe's Test
19.4.5.
Dunnett's Test
19.5.
Two-Way ANOVA
19.5.1.
Main Effects
19.5.2.
Interaction Effects
19.5.3.
Factorial Design
19.5.4.
Implementation in R
19.5.5.
Interpretation
19.6.
Repeated Measures ANOVA
19.6.1.
Within-subjects Design
19.6.2.
Sphericity Assumption
19.6.3.
Implementation
19.7.
Nonparametric ANOVA
19.7.1.
Kruskal-Wallis Test
19.7.2.
Friedman Test
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18. Chi-squared Tests
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20. Correlation Analysis