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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
24.
Generalized Linear Models
24.1.
GLM Framework
24.1.1.
Exponential Family
24.1.2.
Link Functions
24.1.3.
Linear Predictor
24.1.4.
Deviance
24.2.
Logistic Regression
24.2.1.
Binary Outcomes
24.2.2.
Logit Link Function
24.2.3.
Odds and Odds Ratios
24.2.4.
Model Fitting
24.2.5.
Interpretation
24.2.6.
Prediction
24.2.7.
Model Assessment
24.3.
Poisson Regression
24.3.1.
Count Data
24.3.2.
Log Link Function
24.3.3.
Rate Models
24.3.4.
Overdispersion
24.3.5.
Model Fitting
24.3.6.
Interpretation
24.4.
Other GLM Families
24.4.1.
Gamma Regression
24.4.2.
Inverse Gaussian
24.4.3.
Quasi-likelihood Models
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23. Multiple Linear Regression
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25. Nonparametric Statistics