Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
Arrow right icon
View More author details
Toc

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

Summary


Whew, we've been through a lot in this chapter, and I commend you for sticking it out. Your tenacity will be well rewarded when you start using regression analysis in your own projects or to research like a professional.

We started off with the basics: how to describe a line, simple linear relationships, and how a best-fit regression line is determined. You saw how we can use R to easily plot these best-fit lines.

We went on to explore regression analysis with more than one predictor. You learned how to interpret the loquacious lm summary output, and what everything meant. In the context of multiple regression, you learned how the coefficients are properly interpreted as the effect of a predictor controlling for all other predictors. You're now aware that controlling for and thinking about confounds is one of the cornerstones of statistical thinking.

We discovered that we weren't limited to using continuous predictors, and that, using dummy coding, we can not only model the effects...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image