Read more about Linear Regression Using R: An Introduction to Data Modeling

Linear Regression Using R: An Introduction to Data Modeling

(6 reviews)

David J. Lilja, University of Minnesota

Copyright Year: 2016

ISBN 13: 9781946135001

Publisher: University of Minnesota Libraries Publishing

Language: English

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Reviewed by Jiaying Liu, Assistant Professor, University of Southern Indiana on 5/12/22

The textbook is thorough enough for students learning regression using R for the first time, which is exactly what it is designed to be. There is no mathematics or statistics involved in explaining regression. Self-learners and students will... read more

Reviewed by Fariba Khoshnasib, Visiting assistant professor, Earlham College on 3/2/21

I was thinking of using this for a course in linear regression but unfortunately this doesn't include any of the mathematics behind the analysis. This can be used simply to learn how to do regression in R in a few hours without any knowledge of... read more

Reviewed by Mark Greenwood, Professor, Montana State University - Bozeman on 12/29/20

It is very focused and short treatment of simple and multiple linear regression. It does not cover categorical predictors, interactions of predictors, doesn't spend much time interpreting slope coefficients, or discuss confidence intervals for... read more

Reviewed by James Squires, Assistant Professor of Economics, Franklin College on 12/19/18

For a introduction/tutorial to linear regressions with R, this book quickly guides a novice to building a linear model and testing it. read more

Reviewed by Robert Leonard, Assistant Professor, Miami University on 2/1/18

There are basic functions such as class() or typeof() that should be introduced early on for any user of R. Also, A practical explanation of residual standard error or what a nonsensical model for the example used throughout the text would be... read more

Reviewed by Jairo Santanilla, Professor, University of New Orleans on 2/8/17

This is a tutorial that covers basic areas and ideas of linear regression. It covers this material through carefully selected examples. R, the software used to present examples in the text, is an open source software which is appropriate and... read more

Table of Contents

1 Introduction

  • 1.1 What is a Linear Regression Model?
  • 1.2 What is R?
  • 1.3 What's Next?

2 Understand Your Data

  • 2.1 Missing Values
  • 2.2 Sanity Checking and Data Cleaning
  • 2.3 The Example Data
  • 2.4 Data Frames
  • 2.5 Accessing a Data Frame

3 One-Factor Regression

  • 3.1 Visualize the Data
  • 3.2 The Linear Model Function
  • 3.3 Evaluating the Quality of the Model
  • 3.4 Residual Analysis

4 Multi-factor Regression

  • 4.1 Visualizing the Relationships in the Data
  • 4.2 Identifying Potential Predictors
  • 4.3 The Backward Elimination Process
  • 4.4 An Example of the Backward Elimination Process
  • 4.5 Residual Analysis
  • 4.6 When Things Go Wrong

5 Predicting Responses

  • 5.1 Data Splitting for Training and Testing
  • 5.2 Training and Testing
  • 5.3 Predicting Across Data Sets

6 Reading Data into the R Environment

  • 6.1 Reading CSV files

7 Summary8 A Few Things to Try NextBibliographyIndex

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  • About the Book

    Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.

    About the Contributors


    David J. Lilja received a Ph.D. and an M.S., both in Electrical Engineering, from the University of Illinois at Urbana-Champaign, and a B.S. in Computer Engineering from Iowa State University in Ames. He is currently the Louis John Schnell Professor of Electrical and Computer Engineering at the University of Minnesota in Minneapolis, where he also serves as a member of the graduate faculties in Computer Science, Scientific Computation, and Data Science. Previously, he served ten years as the head of the ECE department at the University of Minnesota, worked as a research assistant at the Center for Supercomputing Research and Development at the University of Illinois, and as a development engineer at Tandem Computers Incorporated in Cupertino, California. He received a Fulbright Senior Scholar Award to visit the University of Western Australia, and was awarded a McKnight Land-Grant Professorship by the Board of Regents of the University of Minnesota. He has chaired and served on the program committees of numerous conferences, and was a distinguished visitor of the IEEE Computer Society. He was elected a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the American Association for the Advancement of Science (AAAS) for contributions to the statistical analysis of computer performance. He also is a member of the ACM, and is a registered Professional Engineer. His main research interests include computer architecture, parallel processing, computer systems performance analysis, approximate computing, and storage systems.

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