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

Linear regression is perhaps the most important tool in statistics. It can be used in a wide array of situations and can be easily extended to work in those cases where it can't work in principle. Conceptually, the idea is to model a dependent variable in terms of a set of independent variables and capture coefficients that relate each independent variable to the dependent one. The usual formula here is as follows (assuming that we have one variable and an intercept):

Here, the beta and the intercept are coefficients that we need to find. xi is the independent variable, ui is an unobserved residual, and yi is the target variable. The previous formula can naturally be extended to multiple variables. In that case we would have multiple /beta coefficients.

Maybe the most important aspect of linear regression is that we can do very simple yet powerful interpretations...