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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
18.
Chi-squared Tests
18.1.
Goodness-of-Fit Test
18.1.1.
Purpose and Applications
18.1.2.
Expected vs Observed Frequencies
18.1.3.
Test Statistic
18.1.4.
Degrees of Freedom
18.1.5.
Assumptions
18.1.6.
Implementation in R
18.2.
Test of Independence
18.2.1.
Contingency Tables
18.2.2.
Expected Frequencies
18.2.3.
Test Statistic
18.2.4.
Degrees of Freedom
18.2.5.
Implementation in R
18.2.6.
Measures of Association
18.3.
Test of Homogeneity
18.3.1.
Purpose and Applications
18.3.2.
Comparison with Independence Test
18.3.3.
Implementation
18.4.
Fisher's Exact Test
18.4.1.
When to Use
18.4.2.
Small Sample Sizes
18.4.3.
Implementation in R
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17. Two-Sample Tests
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19. Analysis of Variance (ANOVA)