Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 7. Resampling Methods

 

"Dear friend, theory is all gray, and the golden tree of life is green."

 
 --– Johann Wolfgang von Goethe, Faust

For a lot of people, classical statistical inference is hard to understand, because it is packed with mathematics. Moreover, it is often very difficult and complex to demonstrate the properties of even relatively simple estimators in an analytical manner. Often it is even impossible to express the properties of estimators using mathematical formulas.

In the case of estimating confidence intervals or by carrying out a statistical test, distribution requirements must be assumed when applying classical statistics, basically the distribution of a test statistic. The mathematical formulation for estimating a classical confidence interval for a parameter (and point estimate ) can often be very complex or even impossible. Imagine you want classical confidence intervals not only for the very simple arithmetic mean, but also for the median, for the 10 percent...