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
20.
Correlation Analysis
20.1.
Correlation Concepts
20.1.1.
Linear Relationships
20.1.2.
Strength and Direction
20.1.3.
Correlation vs Causation
20.2.
Pearson Correlation
20.2.1.
Assumptions
20.2.2.
Calculation
20.2.3.
Interpretation
20.2.4.
Significance Testing
20.2.5.
Implementation in R
20.3.
Spearman Rank Correlation
20.3.1.
When to Use
20.3.2.
Calculation
20.3.3.
Implementation in R
20.3.4.
Comparison with Pearson
20.4.
Kendall's Tau
20.4.1.
Calculation
20.4.2.
Implementation
20.4.3.
When to Use
20.5.
Partial Correlation
20.5.1.
Controlling for Third Variables
20.5.2.
Implementation
20.6.
Correlation Matrices
20.6.1.
Multiple Variables
20.6.2.
Visualization
20.6.3.
Missing Data Handling
20.7.
Correlation Assumptions and Diagnostics
20.7.1.
Linearity
20.7.2.
Outliers
20.7.3.
Restriction of Range
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19. Analysis of Variance (ANOVA)
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21. Simple Linear Regression