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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
Nonparametric Statistics
When to Use Nonparametric Tests
Assumption Violations
Ordinal Data
Small Sample Sizes
Rank-Based Tests
Ranking Procedures
Tied Values
One-Sample Nonparametric Tests
Sign Test
Wilcoxon Signed-Rank Test
Two-Sample Nonparametric Tests
Mann-Whitney U Test
Wilcoxon Rank-Sum Test
Multiple Sample Tests
Kruskal-Wallis Test
Friedman Test
Nonparametric Correlation
Spearman's Rank Correlation
Kendall's Tau
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24. Generalized Linear Models
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26. Introduction to Time Series Analysis