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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 7. Going Further with Regression Models

In previous chapters, we learned to use different regression models to analyze different types of data. We have therefore fully understood the concept that proposes a regression algorithm for each event, which means that data is not all equal and for each data collection there is a regression algorithm that allows extracting knowledge. Numeric data with many predictors must be treated differently from the data that has only one predictor. Just as, different tools have to be adopted in the presence of categorical data, as well as when we handle data with dichotomous responses. We can safely assert that there is a more suited regression algorithm for each type of data and that a predictive analysis of the data in our possession is crucial to addressing our search for the most suitable algorithm.

Based on what we have said, in this chapter we will continue to deepen regression algorithms by introducing new regression techniques that are particularly...

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