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

Horton, Nicholas J.,Kleinman, Ken

ISBN 10: 1466584491 / ISBN 13: 9781466584495

Editore: Chapman and Hall/CRC, 2014

Riguardo questo articolo

Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Codice articolo S_401429149

Dati bibliografici

• Titolo: SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1466584491
• ISBN 13: 9781466584495
• Rilegatura: Hardcover
• Edizione: seconda edizione
• Condizione: Good

Descrizione articolo

Riassunto

An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily 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 graphics, along with more complex applications.

New to the Second EditionThis edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples.

Enables Easy Mobility between the Two SystemsThrough the extensive indexing and cross-referencing, users can directly find and implement the material they need. 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. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

Informazioni sull?autore

Ken Kleinman, Nicholas J. Horton