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Data Analysis with R, Second Edition - Second Edition

You're reading from Data Analysis with R, Second Edition - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788393720

Pages 570 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (24) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

RefresheR

The Shape of Data

Describing Relationships

Probability

Using Data To Reason About The World

Testing Hypotheses

Bayesian Methods

The Bootstrap

Predicting Continuous Variables

Predicting Categorical Variables

Predicting Changes with Time

Sources of Data

Dealing with Missing Data

Dealing with Messy Data

Dealing with Large Data

Working with Popular R Packages

Reproducibility and Best Practices

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Index

Exercises

The following are a few exercises for you to strengthen your grasp of the concepts learned in this chapter:

Normally, when there is missing data for a question such as "What is your income?", we strongly suspect an MNAR mechanism, because we live in a dystopia that equates wealth with worth. As a result, the participants with the lowest income may be embarrassed to answer that question. In the relevant section, we assumed that because the question was poorly worded and we could account for whether English was the first language of the participant, the mechanism is MAR. If we were wrong about this reason, and it was really because the lower income participants were reticent to admit their income, what would the missing data mechanism be now? If, however, the differences in income were fully explained by whether English was the first language of the participant, what would the missing data mechanism be in that case?
Find a dataset on the web with missing data. What does it use to denote...

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