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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

What we didn't cover

In an effort to spend more time laying a foundation and facilitating a deeper understanding of one of the most popular intermediate methods of forecasting (and, even as it is, I couldn't go into nearly as much detail as I would have liked to for want of space), we necessarily had to miss out on a few topics that would have been nice and helpful to cover. Particularly, the primary topic that comes to mind is ARIMA, or autoregressive integrated moving average, models.

ARIMA models, like exponential smoothing methods as of the late last and early this century, are often expressed as state space models. In addition, many of the exponential smoothing methods we used in this chapter can be translated to equivalent ARIMA models (this is, in fact, how many software programs provided prediction intervals for exponential smoothing forecasts before the state...

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