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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

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 Other Books You May Enjoy Index

Exercises


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

  • Write a function that will take a vector holding MCMC samples for a parameter and plot a density curve depicting the posterior distribution and the 95% credible interval. Be careful of different scales on the y-axis.
  • Fitting a normal curve to an empirical distribution is conceptually easy, but not very robust. For distribution fitting that is more robust to outliers, it's common to use a t-distribution instead of the normal distribution, since the t has heavier tails. View the distribution of the shape attribute of the built-in rock dataset. Does this look normally distributed? Find the parameters of a normal curve that is a fit to the data. In JAGS, dt, the t-distribution density function, takes three parameters: the mean, the precision, and the degrees of freedom that controls the heaviness of the tails. Find the parameters after fitting a t-distribution to the data. Are the means similar? Which...
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