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© 2018

Statistical Data Analysis Using SAS

Intermediate Statistical Methods

Benefits

  • Integrates SAS programming with complex data analysis applications

  • Focuses on using SAS and statistical aspects of models and methods of analysis

  • Introduction to SAS - beyond the basics and illustrated with numerous worked examples

  • Advanced material suitable for a second course in applied statistics with every method explained using a SAS analysis to illustrate a real-world problem

  • 15-20 problems in every chapter

  • End of chapter exercises

  • Solutions to exercises

  • Downloadable SAS code and data sets

Textbook
  • 21k Downloads

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 1-68
  3. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 69-146
  4. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 147-197
  5. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 199-299
  6. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 301-417
  7. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 419-528
  8. Mervyn G. Marasinghe, Kenneth J. Koehler
    Pages 529-619
  9. Back Matter
    Pages 621-679

About this book

Introduction

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data.

The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude.

Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem.

New to this edition:

•    Covers SAS v9.2 and incorporates new commands
•    Uses SAS ODS (output delivery system) for reproduction of tables and graphics output
•    Presents new commands needed to produce ODS output
•    All chapters rewritten for clarity
•    New  and updated examples throughout
•    All SAS outputs are new and updated, including graphics
•    More exercises and problems
•    Completely new chapter on analysis of nonlinear and generalized linear models
•    Completely new appendix

Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing.

Kenneth J. Koehler, PhD
, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

Keywords

statistical modeling SAS data analysis programming statistics analysis of random models analysis of mixed models statistical graphics

Authors and affiliations

  1. 1.Department of StatisticsIowa State UniversityAmesUSA
  2. 2.Department of StatisticsIowa State UniversityAmesUSA

About the authors

Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. He has taught a data analysis course based on SAS for over 35 years and offered workshops and short-courses on various aspects of SAS including traditional SAS programming, SAS Enterprise Guide, SAS Enterprise Miner, and JMP for many years to both university audiences and non-academic participants.

Kenneth J. Koehler, PhD
, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

Bibliographic information

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