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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
Toc

Table of Contents (24) Chapters close

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 Index

Exercises


Practice the following exercises to revise the concepts learned in this chapter:

  • Write a function that takes a vector and returns the 95 percent confidence interval for that vector. You can return the interval as a vector of length two: the lower bound and the upper bound. Then, parameterize the confidence coefficient by letting the user of your function choose their own confidence level, but keep 95 percent as the default. Hint: the first line will look like this:
conf.int <- function(data.vector, conf.coeff=.95){ 
  • Back when we introduced the central limit theorem, I said that the sampling distribution from any distribution would be approximately normal. Don't take my word for it! Create a population that is uniformly distributed using the runif function and plot a histogram of the sampling distribution using the code from this chapter and the histogram-plotting code from Chapter 2The Shape of Data. Repeat the process using the beta distribution with parameters (a=0.5, b=0.5...
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