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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

White noise


I'd like to talk about a very special time series in this section: the white noise series. White noise is an important topic of physics, psychoacoustics, computer science, electronics, and even medicine. As you might expect, the concept of the white noise series is an important fundamental in time series analysis and forecasting, as well.

If you've ever turned your radio on between stations, you've heard white noise in action but what is it really?

White noise (in the strict sense of the term) is a random series whose samples are identically distributed and statistically independent. For example, the time series we made from sampling from the normal distribution 100 times was a white noise series. At every point in time, the mean of the distribution which gave rise to the samples was constant, and every sample was completely independent of the sample before it.

More specifically, the series we created from random sampling is called a Gaussian white noise series, since the observations...

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