Preamble
1 Axioms of Probability Theory
- 1.1 Manipulation of Sets
- 1.2 Venn and Euler diagrams
2 Discrete Probability Spaces
3 Distributions on the Real Line
4 Discrete Distributions
5 Continuous Distributions
6 Multivariate Distributions
7 Expectation and Concentration
- 7.1 Concentration inequalities
8 Convergence of Random Variables
9 Stochastic Processes
10 Sampling and Simulation
11 Data Collection
12 Models, Estimators, and Tests
- 12.1 Confidence intervals
- 12.2 Tests
13 Properties of Estimators and Tests
- 13.1 Comparing estimators
- 13.2 Uniformly most powerful tests
14 One Proportion
- 14.1 Comparing various confidence intervals
15 Multiple Proportions
16 One Numerical Sample
17 Multiple Numerical Samples
- 17.1 An example of A/B testing
18 Paired Numerical Samples
19 Correlation Analysis
20 Multiple Testing
- 20.1 An example from genetics
  - 20.1.1 Adjusted p-values
  - 20.1.2 Rejections
- 20.2 An example from meta-analysis
  - 20.2.1 Cochran–Mantel–Haenszel test
  - 20.2.2 Combination tests
21 Regression Analysis
- 21.1 Regression
- 21.2 Classification
  - 21.2.1 Nearest neighbor classifier
  - 21.2.2 Linear classification
22 Foundational Issues

Principles of Statistical Analysis: R Companion

3 Distributions on the Real Line

We will see examples of distributions on the real line in the next chapters, so that there is nothing in particular to illustrate here.