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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
Author (1):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Toc

Table of Contents (15) Chapters close

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 Index

Chapter 4. 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 a phenomenon. The term classification can cover any context in which certain decisions or forecasts are made on the basis...

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