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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

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Point Conditioning

Contents

10.2.1. Terminology Review

10.2. Point Conditioning#

10.2.1. Terminology Review#

Use the flashcards below to help you review the terminology introduced in this chapter. \(~~~~ ~~~~ ~~~~ \mbox{ }\)

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10.1. Binary Decisions from Continuous Data: Non-Bayesian Approaches

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10.3. Optimal Bayesian Decision Making with Continuous Random Variables

Contents

10.2.1. Terminology Review

By John M. Shea

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