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Regression Analysis with R

You're reading from Regression Analysis with R

Product type Book

Published in Jan 2018

Publisher Packt

ISBN-13 9781788627306

Pages 422 pages

Edition 1st Edition

Languages

Concepts

Predictive Analytics

Author (1):

Giuseppe Ciaburro

Table of Contents (15) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Getting Started with Regression

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

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Index

Chapter 2. 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 that has been collected about a group of bikers: number of years of use, number of kilometers traveled in 1 year, and number of falls...

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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