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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

1. RefresheR

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

1. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

Exercises

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

Is multiple imputation amenable to parallel computation? Why, or why not?
How is the way we call to.radians wasteful? Is there any way to refactor our code to use to.radians in a more efficient way?
When I was gathering the data from Figure 12.2, I didn't check every sample size from 1 to the full data set; yet, I've obtained a smooth curve. What I did was test the performance of a handful of sample sizes from 100 to only 2,000. Then I used nls (non-linear least squares) to fit an equation of the form
(where n is the sample size) to the data points, and extrapolated with this equation after solving for x. What are some benefits and drawbacks of this approach? Do this on your own machine, if applicable. Do your performance curves match mine?
There is a thought among some scholars that there is an incongruence between Adam Smith's two seminal works, The Wealth of Nations and The Theory of Moral Sentiments, namely...

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