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)

Finding correlation between the features

In a linear model, the correlation between the features increases the variance for the associated parameters (the parameters related to those variables). The more correlation we have, the worse it is. The situation is even worse when we have almost perfect correlation between a subset of variables: in that case, the algorithm that we use to fit linear models doesn't even work. The intuition is the following: if we want to model the impact of a discount (yes-no) and the weather (rain–not rain) on the ice cream sales for a restaurant, and we only have promotions on every rainy day, we would have the following design matrix (where Promotion=1 is yes and Weather=1 is rain):

Promotion Weather
1 1
1 1
0 0
0 0

This is problematic, because every time one of them is 1, the other is 1 as well. The model cannot identify...