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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

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788627306

Length 422 pages

Edition 1st Edition

Languages

R

Concepts

Design

Author (1):

Giuseppe Ciaburro

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Table of Contents (11) Chapters

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

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Multivariate Adaptive Regression Splines

MARS is a form of regression analysis introduced by Jerome H. Friedman (1991), with the main purpose being to predict the values of a response variable from a set of predictor variables.

MARS is a nonparametric regression procedure that makes no assumption about the underlying functional relationship between the response and predictor variables.

This relationship is constructed from a set of coefficients and basis functions that are processed starting from the regression data. The method divides the input space into regions, each with its own regression equation. This makes MARS particularly suitable for problems with a large number of predictors. The following figure shows a distribution with two regression regions:

The MARS algorithm operates as a multiple piecewise linear regression, where each breakpoint (estimated from the data) defines the region of application for a very simple linear regression equation.

The general MARS model equation is as follows...

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Authors (1)

Giuseppe Ciaburro

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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