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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices 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}. Since 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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