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A handbook of statistical analyses using SAS [Book] / Geoff Der and Brian S. Everitt.

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, c2009.Edition: 3rd edDescription: xv, 391 p. : ill. ; 24 cmISBN:
  • 1584887842 (hardback : alk. paper)
  • 9781584887843 (hardback : alk. paper)
Subject(s): DDC classification:
  • 519.5 22
Other classification:
  • 519.5
Summary: Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis. They demonstrate the analyses through real-world examples, including methadone maintenance treatment, the relation of cirrhosis deaths to alcohol consumption, a sociological study of children, heart transplant treatment, and crime rate determinants. With the data sets and SAS code available online, this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books Books Junaid Zaidi Library, COMSATS University Islamabad Ground Floor 519.5 DER-H (Browse shelf(Opens below)) Available 47272
Total holds: 0

"A Chapman & Hall book."

Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis. They demonstrate the analyses through real-world examples, including methadone maintenance treatment, the relation of cirrhosis deaths to alcohol consumption, a sociological study of children, heart transplant treatment, and crime rate determinants. With the data sets and SAS code available online, this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.

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