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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

Exercises


Practice the following exercises to revise the concepts learned thus far:

  • By far, the best way to become comfortable and learn the ins and outs of applied regression analysis is to actually carry out regression analyses. To this end, you can use some of the many datasets that are included in R. To get a full listing of the datasets in the datasets package, execute the following code:
  help(package="datasets") 
  • There are hundreds more datasets spread across the other several thousand R packages. Even better, load your own datasets, and attempt to model them.
  • Examine and plot the  pressure dataset, which describes the relationship between the vapor pressure of mercury and temperature. What assumption of linear regression does this violate? Attempt to model this using linear regression by using temperature squared as a predictor, as shown in the following code:
  lm(pressure ~ I(temperature^2), data=pressure) 
  • Compare the fit between the model that uses the non-squared temperature and...
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