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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

Regression with a non-binary predictor


Back in a previous section, I promised that the same dummy-coding method that we used to regress binary categorical variables could be adapted to handle categorical variables with more than two values. For an example of this, we are going to use the same WeightLoss dataset as we did in to illustrate ANOVA.

To review, the WeightLoss dataset contains pounds lost and self-esteem measurements for three weeks for three different groups: a control group, one group just on a diet, and one group that dieted and exercised. We will be trying to predict the amount of weight lost in week 2 by the group the participant was in.

Instead of just having one dummy-coded predictor, we now need two. Specifically:

Consequently, the equations describing our predictive model are:

Meaning that the is the mean of weight lost in the control group, is the difference in the weight lost between control and diet only group, and is the difference in the weight lost between the control...

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