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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
17.
Two-Sample Tests
17.1.
Independent Samples t-test
17.1.1.
Assumptions
17.1.2.
Equal Variances
17.1.3.
Unequal Variances
17.1.4.
Welch's t-test
17.1.5.
Implementation in R
17.1.6.
Effect Size
17.2.
Paired Samples t-test
17.2.1.
When to Use
17.2.2.
Assumptions
17.2.3.
Test Statistic
17.2.4.
Implementation in R
17.2.5.
Comparison with Independent Samples
17.3.
Two-Sample Proportion Test
17.3.1.
Assumptions
17.3.2.
Test Statistic
17.3.3.
Implementation in R
17.3.4.
Fisher's Exact Test
17.4.
Tests for Equal Variances
17.4.1.
F-test
17.4.2.
Levene's Test
17.4.3.
Bartlett's Test
17.5.
Nonparametric Two-Sample Tests
17.5.1.
Mann-Whitney U Test
17.5.2.
Wilcoxon Rank-Sum Test
17.5.3.
Kolmogorov-Smirnov Test
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18. Chi-squared Tests