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
14.
Statistical Inference Foundations
14.1.
Point Estimation
14.1.1.
Estimators and Estimates
14.1.2.
Properties of Estimators
14.1.2.1.
Unbiasedness
14.1.2.2.
Efficiency
14.1.2.3.
Consistency
14.1.3.
Method of Moments
14.1.4.
Maximum Likelihood Estimation
14.2.
Interval Estimation
14.2.1.
Confidence Intervals
14.2.2.
Confidence Level
14.2.3.
Margin of Error
14.2.4.
Interpretation of Confidence Intervals
14.3.
Confidence Intervals for Means
14.3.1.
Known Population Variance
14.3.2.
Unknown Population Variance
14.3.3.
Small Sample Sizes
14.3.4.
One-sample Intervals
14.3.5.
Two-sample Intervals
14.4.
Confidence Intervals for Proportions
14.4.1.
Single Proportion
14.4.2.
Difference in Proportions
14.4.3.
Sample Size Requirements
14.5.
Confidence Intervals for Variance
14.5.1.
Chi-squared Distribution
14.5.2.
Single Sample Variance
14.5.3.
Ratio of Variances
Previous
13. Sampling and Sampling Distributions
Go to top
Next
15. Hypothesis Testing Framework