Book Image

R Statistics Cookbook

By : Francisco Juretig
2 (2)
Book Image

R Statistics Cookbook

2 (2)
By: Francisco Juretig

Overview of this book

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
Table of Contents (12 chapters)

Introduction

Classical statistical methods don't handle outliers well. The worst part is that even the most basic methods suffer this problem: for example, the sample mean, which is the maximum likelihood estimate for the µ parameter (assuming that the distribution is Gaussian), can be wrong even with a single contaminated observation. For example, the average between the numbers: 3, 4, and 5, is 4; and if we replace that last value (=5) with a new contaminated value =100, the new average will be =107/3. Let's introduce the concept of breakdown point for an estimator. The breakdown point (of an estimator) is the proportion of values that the estimator can handle before yielding wrong results. In the case of the mean that we just explained, the breakdown is 0; meaning that even a single contaminated observation would make the estimator give wrong results. The median...