UsefulLinks
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
22.
Regression Diagnostics
22.1.
Regression Assumptions
22.1.1.
Linearity
22.1.2.
Independence
22.1.3.
Homoscedasticity
22.1.4.
Normality of Residuals
22.2.
Residual Analysis
22.2.1.
Types of Residuals
22.2.2.
Residual Plots
22.2.3.
Standardized Residuals
22.2.4.
Studentized Residuals
22.3.
Diagnostic Plots
22.3.1.
Residuals vs Fitted
22.3.2.
Normal Q-Q Plot
22.3.3.
Scale-Location Plot
22.3.4.
Residuals vs Leverage
22.4.
Outliers and Influential Points
22.4.1.
Outlier Detection
22.4.2.
Leverage
22.4.3.
Cook's Distance
22.4.4.
DFBETAS
22.4.5.
Influence Measures
22.5.
Addressing Assumption Violations
22.5.1.
Transformations
22.5.2.
Robust Regression
22.5.3.
Alternative Models
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21. Simple Linear Regression
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23. Multiple Linear Regression