Simulating Data with SAS

Wicklin, Rick

ISBN 10: 1612903320 / ISBN 13: 9781612903323

Published by SAS Institute, 2013

About this Item

The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.

Seller Inventory # BVV.1612903320.G

Bibliographic Details

• Title: Simulating Data with SAS
• Publisher: SAS Institute
• Publication Date: 2013
• Language: English
• ISBN 10: 1612903320
• ISBN 13: 9781612903323
• Binding: Soft cover
• Condition: good

About this title

Synopsis

Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers.

This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners.

As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software.

About the Author

Rick Wicklin is a principal researcher in computational statistics at SAS, where he develops and supports the IML procedure and the SAS/IML Studio application. He received a PhD from Cornell University and has been a SAS user since 1997. Rick has presented numerous tutorials and papers at statistical and SAS users group conferences and is active in the American Statistical Association. Rick maintains a blog for statistical programmers at blogs.sas.com/content/iml/.