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  • Book Overview & Buying Data Analysis with R, Second Edition
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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition - Second Edition

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Data Analysis with R, Second Edition

Data Analysis with R, Second Edition

3.5 (2)

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 (19 chapters)
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Bootstrapping statistics other than the mean


In the conclusion of the section What's... uhhh... the deal with the bootstrap?, I briefly touched on two important points. The first was an ominous and unexplained implication that a parametric distribution describing the sampling distribution of a statistic of interest may not exist. The second was a promise that even if, for example, the bootstrap distribution of means were identical to the t-distribution in all cases, there would still be great merit in learning how to wield the bootstrap. In this section, I hope to make clear these two points.

First, let's think back to all the tests of means we performed in Chapter 6, Testing Hypotheses. Let's ask ourselves why we wanted to test equality of means. It is certainly true that the arithmetic mean is one of the most common, if not the most common measures of central tendency and, indeed, in all of statistics. But why is it that we are always testing means? May it not be useful to ask (and test...

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Data Analysis with R, Second Edition
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