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Data Analysis with R, Second Edition - Second Edition

You're reading from Data Analysis with R, Second Edition - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788393720

Pages 570 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (24) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

RefresheR

The Shape of Data

Describing Relationships

Probability

Using Data To Reason About The World

Testing Hypotheses

Bayesian Methods

The Bootstrap

Predicting Continuous Variables

Predicting Categorical Variables

Predicting Changes with Time

Sources of Data

Dealing with Missing Data

Dealing with Messy Data

Dealing with Large Data

Working with Popular R Packages

Reproducibility and Best Practices

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Index

Simple linear regression

Onto a substantially less trivial example; let's say No Scone Unturned has been keeping careful records of how many raisins (in grams) they have been using for their famous oatmeal raisin cookies. They want to construct a linear model describing the relationship between the area of a cookie (in centimeters squared) and how many raisins they use, on average.

In particular, they want to use linear regression to predict how many grams of raisins they will need for a 1-meter long oatmeal raisin cookie. Predicting a continuous variable (grams of raisins) from other variables sounds like a job for regression! In particular, when we use just a single predictor variable (the area of the cookies), the technique is called simple linear regression.

The left panel of Figure 9.2 illustrates the relationship between the area of cookies and the amount of raisins it used. It also shows the best-fit regression line:

Figure 9.2: A scatterplot of areas and grams of raisins in No Scone...

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