#### 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
Free Chapter
Introduction
R and High-Performance Computing
The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions
Simulation of Random Numbers
Monte Carlo Methods for Optimization Problems
Probability Theory Shown by Simulation
Resampling Methods
Applications of Resampling Methods and Monte Carlo Tests
The EM Algorithm
Simulation with Complex Data
System Dynamics and Agent-Based Models
Index

## The central limit theorem

The classical theory of sampling is based on the following fundamental theorem.

### Tip

When the distribution of any population has finite variance, then the distribution of the arithmetic mean of random samples is approximately normal, if the sample size is sufficiently large.

The proof of this theorem is usually about 3-6 pages (using advanced mathematics on measure theory). Rather than doing this mathematical exercise, the "proof" is done by simulation, which also helps to understand the central limit theorem and thus the basics of statistics.

The following setup is necessary:

• We draw samples from populations. This means that we know the populations. This is not the case in practice, but we show that the population can have any distribution as long as the variance is not infinite.

• We draw many samples from the population. Note that in practice, only one sample is drawn. For simulation purposes, we assume that we can draw many samples.

For the purpose of looking at our defined...