Analyzing Health Data in R for SAS Users

ISBN-13: 9781498795883 / Angielski / Twarda / 2017 / 320 str.

Monika Maya Wahi; Peter Seebach

Opis

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 R
• Demonstrates descriptive statistics as well as linear and logistic regression
• Provides exercise questions and answers at the end of each chapter
• Uses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 data
• Guides the reader on producing a health analysis that could be published as a research report
• Gives an example of hypothesis-driven data analysis
• Provides examples of plots with a color insert

Recenzje

"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 students and researchers with knowledge and experiencing in using SAS language to gain programming proficiency in R language. I highly recommend this very insightful book to statisticians, data scientists, social scientists, psychologists, biologists, public health researchers and practitioners, and clinicians who are familiar with SAS to harness the magnificent power of R. I would use this book as a major reference book for a biostatistics course on R."~Tianhua Niu, Tulane University School of Medicine

Spis treści

Differences Between SAS and R. Preparing Data for Analysis. Basic Descriptive Analysis. Basic Regression Analysis.