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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR 2. The Shape of Data FREE CHAPTER 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 18. Other Books You May Enjoy

Testing more than two means


Another really common situation requires testing whether three or more means are significantly discrepant. We would find ourselves in this situation if we had three experimental conditions in the blood pressure trial: one groups gets a placebo, one group gets a low dose of the real medication, and one groups gets a high dose of the real medication.

Hmm, for cases like these, why don't we just do a series of t-tests? For example, we can test the directional alternative hypotheses:

 

  • The low dose of blood pressure medication lowers BP significantly more than the placebo

 

  • The high dose of blood pressure medication lowers BP significantly more than the low dose

Well, it turns out that doing this first is pretty dangerous business, and the logic goes like this: if our alpha level is 0.05, then the chances of making a Type I error for one test is 0.05; if we perform two tests, then our chances of making a Type I error is suddenly .09025 (near 10%). By the time we perform...

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