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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

1. Other Books You May Enjoy

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Index

Count data model

Poisson regression is a form of regression used to model the count of data in contingency tables. For example, counting the number of births or the number of wins in a series of soccer matches. Poisson regression assumes that the response variable Y has a Poisson distribution, and that the logarithm of its expected value can be modelled by a linear combination of unknown parameters. Poisson regression is sometimes also known as a log-linear model, especially when it is used to model contingency tables.

Poisson distributions

A distribution tells us how measures of a certain variable are distributed among the various possible values. Each distribution is characterized by an average value and a variance, which adjusts the uncertainty of the measurements obtained. Poisson's distribution, also known as rare event law, is a very useful type of distribution when dealing with extremely rare events, which occur with a well-defined temporal mean. It is an approximation of the binomial...

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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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