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Data Analysis with R, Second Edition - Second Edition

You're reading from Data Analysis with R, Second Edition - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788393720

Pages 570 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (24) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. RefresheR

2. The Shape of Data

3. Describing Relationships

4. Probability

5. Using Data To Reason About The World

6. Testing Hypotheses

7. Bayesian Methods

8. The Bootstrap

9. Predicting Continuous Variables

10. Predicting Categorical Variables

11. Predicting Changes with Time

12. Sources of Data

13. Dealing with Missing Data

14. Dealing with Messy Data

15. Dealing with Large Data

16. Working with Popular R Packages

17. Reproducibility and Best Practices

1. Other Books You May Enjoy

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Index

Basic probability

Probability measures the likeliness that a particular event will occur. When mathematicians (us, for now!) speak of an event, we are referring to a set of potential outcomes of an experiment, or trial, to which we can assign a probability of occurrence.

Probabilities are expressed as a number between 0 and 1 (or as a percentage out of 100). An event with a probability of 0 denotes an impossible outcome, and a probability of 1 describes an event that is certain to occur.

The canonical example of probability at work is a coin flip. In the coin flip event, there are two outcomes: the coin lands on heads or the coin lands on tails. Pretending that coins never land on their edge (they almost never do), those two outcomes are the only ones possible. The sample space (the set of all possible outcomes), therefore, is {heads, tails}. As the entire sample space is covered by these two outcomes, they are said to be collectively exhaustive.

The sum of the probabilities of collectively...

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