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

Confidence intervals


Now that we have this new, empirically determined sampling distribution (the bootstrap distribution), we can provide interval estimates of population parameters in much the same idea as we can with parametrically determined sampling distributions.

For both the parametric sampling distribution of sample means and the bootstrap distribution of sample means, the range of values from the 2.5% quantile and the 97.5% quantile of the sampling distribution represent the range of values for which 95% of the sample means would lie if repeated samples were taken from the population.

Note

Note that the bootstrap, at least as introduced thus far, is a frequentist technique, and therefore does not use credible intervals like those of the last chapter.

In Chapter 5Using Data To Reason About The World, we saw that we can get 95% confidence intervals by getting the quantile of the t-distribution of our particular sample size, and using this to multiply with the standard error. With the...