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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 FREE CHAPTER 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 18. Other Books You May Enjoy

Exercises

  • Learn more about the other measures of accuracy provided by forecast function's accuracy function. What advantages does MAPE have over RMSE/MAE
  • Learn how to interpret the alpha, beta, gamma, (and sometimes phi) parameters of an ETS object. What does it mean to have a low beta parameter? Try to explicitly specify extreme values for some of these parameters using named arguments in the ets function. How does it affect the quality and dynamics of your forecasts?
  • The GitHub repository that we used to download some of the data sets in this chapter (and we will be using for different data sets for subsequent chapters, as well) also contains a data set containing Google searches for Dr. Martin Luther King, Jr., what example series in this chapter does this most resemble? Is there seasonality in this series? If so, when do the spikes in searches occur...
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