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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
Languages
Concepts
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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

More Than Just One Predictor – MLR

In Chapter 2, Basic Concepts – Simple Linear Regression, we understood the concept of simple linear regression that covers the relationship between only one independent variable (explanatory variable) and the dependent variable (response variable). It's not very often that we find a variable that depends solely on another. Usually, we find that the response variable depends on at least two predictors.

Let's take a look at an example. Getting to the workplace can often be a path full of variables. Scheduling your departure to arrive on time can be a difficult task. That is why you need to take different variables into account: the distance from your home, the type of route to follow (street type), traffic along the route, the number of stops (if you need to drop your children to school), weather conditions, and so on. Have...

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