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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
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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

When the Response Falls into Two Categories – Logistic Regression

In previous chapters, we studied linear regression models in detail. In particular, we found that in all the models described, the response variable takes quantitative values. Often in everyday life, response variables are qualitative instead. For example, we want to determine whether a device is on or off, depending on the noise detected in the environment. Or we want to know whether to issue a credit based on financial information and other personal information. Or we want to diagnose a patient's disease first to select the immediate treatment pending final results.

In each of these cases, we want to explain the probability of having an attribute, or an event occurring, in relation to the number of possible variations of multiple explanatory variables. In other words, we are trying to classify...

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