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)

The Spearman's rank correlation test

The correlation coefficient between X and Y that we usually use is obtained by dividing the covariance of X, Y by the product of the variances of X and Y. It is therefore restricted to lie between -1 and 1. When the correlation is -1, it means that there is a strong negative relationship between the variables. When it is 1, it means that there is a strong positive relationship; and when it is 0, it means that there is no relationship between the variables. But there is an implicit assumption that we usually overlook: the correlation coefficient assumes that there is a linear relationship. So, it is easy to imagine lots of cases where there might be a relationship, but not a linear one.

The Spearman rank statistic does not test correlation in the traditional sense ((whether a greater than average value of X is associated linearly with a...