SAS and R: Data Management, Statistical Analysis, and Graphics

Ken Kleinman

ISBN 10: 1420070576 / ISBN 13: 9781420070576

Editore: Chapman and Hall/CRC, 2009

Riguardo questo articolo

Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Codice articolo Scanned1420070576

Dati bibliografici

• Titolo: SAS and R: Data Management, Statistical Analysis, and Graphics
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2009
• Lingua: Inglese
• ISBN 10: 1420070576
• ISBN 13: 9781420070576
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

An All-in-One Resource for Using SAS and R to Carry out Common Tasks

Provides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and the creation of graphics, along with more complex applications.

Takes an innovative, easy-to-understand, dictionary-like approachThrough the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The book enables easier mobility between the two systems: SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Demonstrating the code in action and facilitating exploration, the authors present extensive example analyses that employ a single data set from the HELP study. They offer the data sets and code for download on the book’s website.

Informazioni sull?autore

Ken Kleinman is an associate professor at Harvard Medical School. His research deals with clustered data analysis, surveillance, and epidemiological applications.

Nicholas J. Horton is an associate professor of statistics at Smith College. His research interests include longitudinal regression models and missing data methods.