9781420070576-1420070576-SAS and R: Data Management, Statistical Analysis, and Graphics

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

ISBN-13: 9781420070576
ISBN-10: 1420070576
Edition: 1
Author: Ken Kleinman, Nicholas J. Horton
Publication date: 2009
Publisher: Chapman and Hall/CRC
Format: Hardcover 343 pages
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Book details

ISBN-13: 9781420070576
ISBN-10: 1420070576
Edition: 1
Author: Ken Kleinman, Nicholas J. Horton
Publication date: 2009
Publisher: Chapman and Hall/CRC
Format: Hardcover 343 pages

Summary

SAS and R: Data Management, Statistical Analysis, and Graphics (ISBN-13: 9781420070576 and ISBN-10: 1420070576), written by authors Ken Kleinman, Nicholas J. Horton, was published by Chapman and Hall/CRC in 2009. With an overall rating of 4.1 stars, it's a notable title among other Mathematical & Statistical (Software) books. You can easily purchase or rent SAS and R: Data Management, Statistical Analysis, and Graphics (Hardcover) from BooksRun, along with many other new and used Mathematical & Statistical books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.81.

Description

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 documentation
SAS 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 approach
Through 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.

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