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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

Preface 1. RefresheR FREE CHAPTER 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. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 18. Other Books You May Enjoy

Relationships between a categorical and continuous variable

Describing the relationship between categorical and continuous variables is perhaps the most familiar of the three broad categories.

When I was in the fifth grade, my class had to participate in an area-wide science fair. We were to devise our own experiment, perform it, and then present it. For some reason, in my experiment, I chose to water some lentil sprouts with tap water and some with alcohol to see if they grew differently.

When I measured the heights and compared the measurements of the teetotaler lentils versus the drunken lentils, I was pointing out a relationship between a categorical variable (alcohol/no-alcohol) and a continuous variable (heights of the seedlings).

Note:
I wasn't trying to make a broader statement about how alcohol affects plant growth. In the grade-school experiment, I was just summarizing...
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