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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
Languages
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

Summary

In this chapter, we introduced classifications through logistic regression techniques. We have first described a logistic model and then we have provided some intuitions underneath the math formulation.  We learned how to define a classification problem and how to apply logistic regression to solve this type of problem. We introduced the simple logistic regression model, where the dichotomous response depends on only one explanatory variable.

Then, we generalized the logistic model to the case of more than one independent variable in the multiple logistic regression. Central arguments in dealing with multiple logistic models have been the estimate of the coefficients in the model and the tests for their significance. This has followed the same lines as the univariate model. In multiple regression, the coefficients are called partial because they express the specific...

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