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Using SAS for Data Management, Statistical Analysis, and Graphics (eBook, PDF) - Kleinman, Ken; Horton, Nicholas J.
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This book presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. It covers data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. The text includes convenient indices organized by topic and SAS syntax and contains many example analyses. Data sets and code are available for download on the book's website. New users of SAS will find the simple approach easy to understand while more expert…mehr

Produktbeschreibung
This book presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. It covers data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. The text includes convenient indices organized by topic and SAS syntax and contains many example analyses. Data sets and code are available for download on the book's website. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.

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Autorenporträt
Ken Kleinman is an associate professor in the Department of Population Medicine at Harvard Medical School in Boston, Massachusetts. His research deals with clustered data analysis, surveillance, and epidemiological applications in projects ranging from vaccine and bioterrorism surveillance to observational epidemiology to individual-, practice-, and community-randomized interventions.

Nicholas J. Horton is an associate professor in the Department of Mathematics and Statistics at Smith College in Northampton, Massachusetts. His research interests include longitudinal regression models and missing data methods, with applications in psychiatric epidemiology and substance abuse research.