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R for Data Science

You're reading from R for Data Science Learn and explore the fundamentals of data science with R

Product type Paperback

Published in Dec 2014

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Dan Toomey

View More author details

Table of Contents (14) Chapters

Preface

1. Data Mining Patterns FREE CHAPTER

2. Data Mining Sequences

3. Text Mining

4. Data Analysis – Regression Analysis

5. Data Analysis – Correlation

6. Data Analysis – Clustering

7. Data Visualization – R Graphics

8. Data Visualization – Plotting

9. Data Visualization – 3D

10. Machine Learning in Action

11. Predicting Events with Machine Learning

12. Supervised and Unsupervised Learning

Index

Summary

In this chapter, we discussed how to perform regression analysis using R. We performed simple regression and analyzed fit, residuals, and other factors. We used multiple regression, including selecting and using a set of predictor values. We tried to determine a set of values from predictors using multivariate regression. Lastly, we used robust regression to overcome possible problems in the predictors and to build a reliable model. In the next chapter, we will be covering correlation.

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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