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Regression Analysis with R

You're reading from   Regression Analysis with R Design and develop statistical nodes to identify unique relationships within data at scale

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788627306
Length 422 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Regression 2. Basic Concepts – Simple Linear Regression FREE CHAPTER 3. More Than Just One Predictor – MLR 4. When the Response Falls into Two Categories – Logistic Regression 5. Data Preparation Using R Tools 6. Avoiding Overfitting Problems - Achieving Generalization 7. Going Further with Regression Models 8. Beyond Linearity – When Curving Is Much Better 9. Regression Analysis in Practice 10. Other Books You May Enjoy

Multiple linear regression with categorical predictor

After dealing with several examples of linear regression, we can certainly claim to have understood the mechanisms underlying this statistical technique. So far, we've used only continuous variables, such as predictors. What happens when the predictors are categorical variables? Don't worry, because the underlying principles of regression techniques remain the same.

Categorical variables

Categorical variables are variables that are not numerical. They do not derive from measurement operations (and do not have units of measurement), but from classification and comparison operations; for instance, they describe data that fits into specific categories. Categorical...

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