Statistical Data Mining Using SAS Applications

Fernandez George

ISBN 10: 1439810753 / ISBN 13: 9781439810750

Editore: Taylor & Francis Group, 2010

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pp. 477 19 Equations - PPI 514 This item is printed on demand.

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Dati bibliografici

• Titolo: Statistical Data Mining Using SAS Applications
• Editore: Taylor & Francis Group
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1439810753
• ISBN 13: 9781439810750
• Rilegatura: Rilegato
• Edizione: seconda edizione
• Condizione: New

Descrizione articolo

Riassunto

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results. Compiled data mining SAS macro files are available for download on the author’s website. By following the step-by-step instructions and downloading the SAS macros, analysts can perform complete data mining analysis fast and effectively.

New to the Second Edition―General Features

• Access to SAS macros directly from desktop
• Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition
• Reorganization of all help files to an appendix
• Ability to create publication quality graphics
• Macro-call error check

New Features in These SAS-Specific Macro Applications

• Converting PC data files to SAS data (EXLSAS2 macro)
• Randomly splitting data (RANSPLIT2)
• Frequency analysis (FREQ2)
• Univariate analysis (UNIVAR2)
• PCA and factor analysis (FACTOR2)
• Multiple linear regressions (REGDIAG2)
• Logistic regression (LOGIST2)
• CHAID analysis (CHAID2)

Requiring no experience with SAS programming, this resource supplies instructions and tools for quickly performing exploratory statistical methods, regression analysis, logistic regression multivariate methods, and classification analysis. It presents an accessible, SAS macro-oriented approach while offering comprehensive data mining solutions.

Informazioni sull?autore

George Fernandez is a professor of applied statistical methods and the director of the Center for Research Design and Analysis at the University of Nevada in Reno.