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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

A one-sample test of means


We finally have enough knowledge under our belts to perform a null-hypothesis significance test using the bootstrap. In fact, given what we already learned, it can scarcely be easier!

As a note, I prefer to use the the bootstrap mainly as a method of generating confidence intervals and illustrating uncertainty in population parameter estimates, and not as a tool for NHST. But, at least as a demonstration, we'll see a few examples of it being used for hypothesis testing here.

For ease of comparison, let's repeat the one sample test that we performed in Chapter 6, Testing Hypotheses. Recall, that the precip built-in dataset contained the precipitation (in inches) of a sample of US cities. We wanted to know if the mean of the population US precipitation was significantly discrepant from the precipitation average of the rest of the world – a value that we, quite unjustifiably, and arbitrarily, said to be 38 inches.

The one sample t-test was performed thusly:

> t.test...
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