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Analyzing health data in R for SAS users

Author: Monika Wahi; Peter Seebach
Publisher: Boca Raton : CRC Press, [2018] ©2018
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R. For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and  Read more...
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Genre/Form: Electronic book
Additional Physical Format: Print version:
(DLC) 2017021131
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Monika Wahi; Peter Seebach
ISBN: 1498795897 9781498795890 9781351394260 1351394266
OCLC Number: 1021405524
Description: 1 online resource (xiv, 318 pages)
Contents: Chapter 1 Differences between SAS and R --
chapter 2 Preparing Data for Analysis --
chapter 3 Basic Descriptive Analysis --
chapter 4 Basic Regression Analysis.
Other Titles: BIOSCIENCEnetBASE.
Responsibility: Monika Wahi and Peter Seebach.

Abstract:

"Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R. For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and provides exercises at the end of each chapter. For students and public health professionals, not only is it a gentle introduction to R, but it can serve as a guide to developing the results for a research report using R software. Features:Gives examples in both SAS and RDemonstrates descriptive statistics as well as linear and logistic regressionProvides exercise questions and answers at the end of each chapterUses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 dataGuides the readifer on producing a health analysis that could be published as a research reportGives an example of hypothesis-driven data analysisProvides examples of plots with a color insert"--Provided by publisher.

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"R is an increasingly popular programming in statistics and data science. This well-presented and timely book builds a critical bridge between SAS and R, which is particularly appropriate for Read more...

 
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