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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. RefresheR 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data to Reason About the World 6. Testing Hypotheses 7. Bayesian Methods 8. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

Linear models


A small baking outfit in upstate New York called No Scone Unturned keeps careful records of the baked goods it produces. The left panel of Figure 8.1 is a scatterplot of diameters and circumferences (in centimeters) of No Scone Unturned's cookies, and depicts their relationship:

Figure 8.1: (left) A scatterplot of diameters and circumferences of No Scone Unturned's cookies; (right) the same plot with a best fit regression line plotted over the data points

A straight line is the perfect thing to represent this data. After fitting a straight line to the data, we can make predictions about the circumferences of cookies that we haven't observed, like 11 or 0.7 (if you weren't playing truant in grade school, you'd know there's a consistent and predictable relationship between the diameter of a circle and the circle's circumference, namely π, but we'll ignore that for now).

You may have learned that the equation that describes a line in a Cartesian plane is:

where is the y-intercept...

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