Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Summary


To review, in this chapter we explored another powerful method for interval estimation and, to a certain extent, hypothesis testing. First, we put this technique in context to be appealing to the similarity with the resampling simulations we performed in earlier chapters. We learned that the bootstrap is a lot like building sampling distributions from population data, but by sampling with replacement from our sample data instead.

We saw examples of the results from the bootstrap procedure, and noted that it is often congruent with results from parametric interval estimation techniques. In spite of this, we learned about what makes the bootstrap different from other alternatives, what makes it special, and an honest look at some of its drawbacks.

After performing the bootstrap manually, to get a thorough handle on how the procedure works, we learned how to perform it in a more elegant and extensible fashion using the boot package. We saw that the objects returned from the boot function...