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 Mann-Whitney test

We have already discussed how to compare the means from two groups, when both groups are distributed according to a Gaussian distribution with the same variance. However, the nonparametric test requires no distributional assumption and works well almost every time. Of course, if both distributions are Gaussian with the same variance, then the regular t-test is better—this is derived from the fact that the t-test is uniformly the most powerful one.

The Mann-Whitney-Wilcoxon test is a nonparametric test that tests the null hypothesis that any element chosen at random from group A is equally likely to be greater or smaller than a respective random item from group B. A different way of posing this test is to think of it as a test of whether the distributions of group A and B are the same. The only strong assumption that this test requires is that the observations...