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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 FREE CHAPTER 2. Basic Concepts – Simple Linear Regression 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

Basic Concepts – Simple Linear Regression

In Chapter 1, Getting Started with Regression, we understood the concept of regression through the basic principles that govern its algorithms. Moreover, we were been able to discover the different types of regression that make it a real family of algorithms that can solve the most varied types of problems. In this book, we will learn more about all of them, but for now, let us begin with the basic concepts from the simpler algorithm, as indicated by its name: simple linear regression.

As we will see, simple linear regression is easy to understand but represents the basis of regression techniques; once these concepts are understood, it will be easier for us to address the other types of regression. To begin with, let's take an example of applying linear regression taken from the real world.

Consider some data...

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