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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
25.
Nonparametric Statistics
25.1.
When to Use Nonparametric Tests
25.1.1.
Assumption Violations
25.1.2.
Ordinal Data
25.1.3.
Small Sample Sizes
25.2.
Rank-Based Tests
25.2.1.
Ranking Procedures
25.2.2.
Tied Values
25.3.
One-Sample Nonparametric Tests
25.3.1.
Sign Test
25.3.2.
Wilcoxon Signed-Rank Test
25.4.
Two-Sample Nonparametric Tests
25.4.1.
Mann-Whitney U Test
25.4.2.
Wilcoxon Rank-Sum Test
25.5.
Multiple Sample Tests
25.5.1.
Kruskal-Wallis Test
25.5.2.
Friedman Test
25.6.
Nonparametric Correlation
25.6.1.
Spearman's Rank Correlation
25.6.2.
Kendall's Tau
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24. Generalized Linear Models
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26. Introduction to Time Series Analysis