Linear Regression Using R: An Introduction to Data Modeling

(1 review)

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David Lilja, University of Minnesota

Pub Date: 2016

ISBN 13: 978-1-9461350-0-1

Publisher: University of Minnesota Libraries Publishing

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Reviewed by Jairo Santanilla, Professor, University of New Orleans, on 2/9/2017.

This is a tutorial that covers basic areas and ideas of linear regression. It covers this material through carefully selected examples. R, the … 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 Summary 
8 A Few Things to Try Next 

Bibliography 
Index 

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

Author(s)

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.