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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

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 Index

Chapter 8. The Bootstrap

In the kind of statistician networks I participate in, there's a huge debate on whether introductory statistics curricula need a profound overhaul. In the particular circles I run in, there's a big movement to shift away from the NHST methods I spoke about in Chapter 6Testing Hypotheses, in favor of either the Bayesian methods we discussed in the previous chapter, or resampling methods such as the bootstrap that we'll be discussing in this chapter.

In spite of my personal ideas on the matter, preferences, and current workflow tendencies, I strongly felt that I would be doing you, dear reader, an enormous disservice leaving out such staples of data analysis as ANOVA and the Student's t-test, especially if you want to make a career of this. For better or worse, the vast majority of new research in science still makes strong use of the techniques stressed by the traditional curriculum. Additionally, these are the type of techniques that are most likely going to be...

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