Using SAS for Data Management, Statistical Analysis and Graphics
- ISBN13:9781439827574
- 出版社:CRC Press UK
- 作者:Ken Kleinman; Nicholas J. Horton
- 裝訂/頁數:平裝/285頁
- 規格:24.1cm*15.2cm*1.9cm (高/寬/厚)
- 出版日:2010/07/28
商品簡介
Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration, the authors present example analyses that employ a single dataset from the HELP study. They also provide several case studies of more complex applications. Datasets and code are available for download on the book's Web site.
Features
Presents worked examples that highlight the most useful portions of the extensive SAS language
Covers various aspects of data management, from reading and exporting data to mathematical functions, logic, coding, and simulations
Describes a wide variety of statistical tests and models, including linear regression and ANOVA, logistic and Poisson regression, models for clustered or correlated data, and more
Shows how to control output using ODS
Highlights the new graphics of SAS 9.2
Helping to improve your analytical skills, this book lucidly summarizes the features of SAS most often used by statistical analysts. 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.
作者簡介
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.
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