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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
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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

Creating a linear regression model

In the previous section, we adopted an algebraic approach to calculating the regression line. More generally, to create a linear regression model, we use the lm() function. This function creates a LinearModel object. The object of class lm has a series of properties that can be immediately viewed by simply clicking on it. These types of objects can be used for residual analysis and regression diagnosis. 

LinearModel is an object comprised of data, model description, diagnostic information, and fitted coefficients for a linear regression.

Models for the lm() function are specified symbolically. In fact, the first argument of the function is an object of class formula. A typical formula object has the following form:

response ~ terms

response represents the (numeric) response vector and terms is a series of terms specifying a linear...

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