Skip to main content

Ctrl+K

Foundations of Data Science with Python

Online Resources for Book Chapters

1. Introduction
2. First Simulations, Visualizations, and Statistical Tests
3. First Visualizations and Statistical Tests with Real Data
4. Introduction to Probability
5. Null Hypothesis Tests
6. Dependence and Independence
7. Introduction to Bayesian Methods
8. Random Variables
9. Moments, Parameter Estimation, and Binary Hypothesis Tests on Sample Means
10. Decision Making with Observations from Continuous Distributions
11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions
12. Multidimensional Data: Vectors and Linear Regression
13. Working with Dependent Data in Multiple Dimensions

Online Resources for Book Chapters

Next Steps
Appendix: Brief Introduction to Sets

Buy the Book

Buy Foundations of Data Science with Python

Repository
Open issue

Conditional Probabilities and Independence in Fair Experiments

6.7. Conditional Probabilities and Independence in Fair Experiments#

Self-Assessment

\(~~~~\mbox{ }\)

previous

6.6. Statistical Independence

next

6.8. Conditioning and (In)dependence

By John M. Shea

© Copyright 2021-2024.