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R Data Analysis Without Programming
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Table of Contents

1. R for Data Analysis 2. Read/Write Data 3. Edit Data 4. Categorical Variables 5. Continuous Variables 6. Means, Compare Two Samples 7. Compare Multiple Samples 8. Correlation 9. Regression I 10. Regression II 11. Factor/Item Analysis

About the Author

David W. Gerbing is a Professor in the School of Business Administration at Portland State University. He has published extensively in psychology, sociology, education, statistics, and business.

Reviews

"R requires a steep learning curve to master. Gerbing's book flattens that learning curve significantly. The breadth of the topics covered, the helpful worked examples, and the ability to move beyond the subject matter covered to other R applications renders this volume of immense value to researchers and provides an excellent text for graduate statistics courses."-Frank Boster, Michigan State University, USA "This book is a straightforward resource for students or data analysts who would like to use R without investing valuable time learning the minutia associated with programming in R. David Gerbing makes an intimidating programming language manageable with examples from real-world scenarios. What sets this book apart is the depth and approachable level in which the output is explained."-Tiffany A. Whittaker, The University of Texas at Austin, USA "My interest in this book was piqued by two features: it uses the computer system R and it seeks to make the system more accessible to data analysts. I have migrated all of my courses to R, and any effort to make R more accessible is to be commended. A quick scan of some sample chapters indicates that the material is attractively presented; this book deserves a serious look from instructors of applied statistics courses."-William B. Ware, University of North Carolina at Chapel Hill, USA

"The book in combination with the lessR package has the potential to lower the bar in terms of initial learning required for working with R. ...The combination of lessR with the ... book is like an "R for dummies". ... lessR. ... It will appeal to users who were ... intimidated by R's complexity. ... I would recommend it to my students and could imagine building an introductory course around it."-Bertolt Meyer, University of Zurich, Switzerland "The book makes a significant contribution to the field. It makes statistics and R available to more people. ... LessR is a very useful package. ... I was able to test some of the functions presented on my own data set and it worked well. ... The author developed a very nice and easy package. ... The book... make[s] using R easier. ... I would ... recommend it to my friends who want to start programming in R ... [and] for my ... course that ... uses R."-Agnieszka Kwapisz, Montana State University, USA "I would use the book as a supplement ... in my introduction to ... statistics course ... [and] recommend it to all graduate students as a good way to teach themselves R. ... The strength of the book is the lessR package and the examples of how to call the functions in the package. ... The book could be an important contribution to the use of R in teaching statistics."-J. Patrick Gray, University of Wisconsin - Milwaukee, USA

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