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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

Summary


Although most introductory data analysis texts don't even broach the topic of Bayesian methods, you, dear reader, are versed enough in this matter to start applying these techniques to real problems.

We discovered that Bayesian methods could—at least for the models in this chapter—not only allow us to answer the same kinds of questions we might use the binomial, one sample t-test, and the independent samples t-test for, but provide a much richer and more intuitive depiction of our uncertainty in our estimates.

If these approaches interest you, I urge you to learn more about how to extend these to supersede other NHST tests. I also urge you to learn more about the mathematics behind MCMC.

As with the last chapter, we covered much ground here. If you made it through, congratulations!

This concludes the unit on confirmatory data analysis and inferential statistics. In the next unit, we will be concerned less with estimating parameters, and more interested in prediction. Last one there is...

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