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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 2. Basic Concepts – Simple Linear Regression FREE CHAPTER 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

Building a multiple linear regression model

In Chapter 2, Basic Concepts – Simple Linear Regression, we learned to use the lm() function to create a simple linear regression model. We can also use it to solve this kind of problem.

To practice this method, we can draw on the many datasets available on the internet. In this case, we will load a .csv file named EscapingHydrocarbons.csv into the R environment; it contains the quantity of hydrocarbons escaping, depending on different variables.

Source: Linear Regression Datasets offered by the Department of Scientific Computing, Florida State University (http://people.sc.fsu.edu/~jburkardt/datasets/regression/regression.html).

When petrol is pumped into tanks, hydrocarbons escape. To evaluate the effectiveness of pollution controls, experiments were performed. The quantity of hydrocarbons escaping was measured...

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