Biostatistics by Example Using SAS® Studio Reviews
I have said it before and I am compelled to say it again; "when you buy a Ron Cody book, you have bought a good book." This is definitely a time to judge a book simply by its author.
SAS Studio is a fairly new interface for SAS that provides easy straightforward, point-and-click access to the power of SAS, and Biostatistics by Example Using SAS® Studio addresses the use of this interface to tackle a number of common statistical techniques. SAS Studio is also the interface used by the SAS University Edition so users of the SAS University Edition will also find direct value in this book.
The basic installation and setup for both SAS Studio and the SAS University Edition are covered in introductory chapters, and because you will, of course be using data, two chapters are devoted to the process of importing data with SAS Studio. The remainder of the book (10 chapters) is devoted to a variety of statistical techniques that will be of value across industries. The techniques range from simple descriptive statistics, to simple tests of hypotheses, to regression analysis, to logistic regression, and even includes a chapter on one of the perennial questions in statistics, the calculation of power and sample size.
As the title implies, the statistical techniques in this book are demonstrated and explained through a series of examples. Although not a statistics book per se, Dr. Cody carefully explains the motivations and reasoning behind the various statistical methods while avoiding statistical jargon and theory. You do not need to be a statistician to be able to understand and utilize the described techniques.
The figures and screenshots in the book are laid out to walk you step-by-step through the approach and options, which include lots of graphs, associated with each of the described techniques. Do you want an introduction to basic statistical techniques? Are you using SAS Studio or the SAS University Edition? If your answers are yes, then you definitely want Biostatistics by Example Using SAS Studio.
Art Carpenter
CALOXY
As a data analyst for a major hospital, I rely on SAS to produce high-quality, accurate analyses quickly and with minimal amount of frustration. Ron Cody is in the rare breed of authors that takes complex ideas and distills them down to the bare essentials. He does this without any of the scary math formulae, complicated jargon, or anything else that distracts me from getting a solid understanding of the concept quickly.
This is a special ability of his that is apparent in all of his books, including Biostatistics by Example Using SAS® Studio. Being able to open the book and create an N-way ANOVA in just a few simple steps is great; having the ability to explain it to my colleagues who are not familiar with analyses is even better.
If you are a student in biostatistics, medical research, epidemiology, or any other healthcare related field and you need to analyze your data, then this book is an essential addition to your bookshelf.
Christopher Battiston
Wait Times Coordinator
Women's College Hospital
One of the beauties of SAS Studio is its seamless integration of a somewhat familiar SAS programming environment--editor, log, and output--with a task-based, point-and-click environment. Ron Cody's Biostatistics by Example Using SAS® Studio provides a wonderful introduction to this environment, especially for traditional SAS programmers, because learning from examples is fun and the book's examples are in the context of the work we do every day: prepare data and then analyze it. Cody doesn't ignore the slight complexities of working on Windows with SAS that's actually running in a Linux environment; instead, he shows how to cope with those complexities (for example, in assigning librefs) so that they are no longer an issue.
One advantage of a task-based interface is that a task can go beyond the output that any single SAS procedure provides or at least provides by default. Many of Cody's examples illustrate this, especially in the area of graphics to check data quality, check assumptions, and understand analysis results. Cody provides just enough statistical background to help you interpret important parts of the SAS output and understand how SAS Studio promotes sound analyses. I also like his frequent inclusion of both parametric and non-parametric approaches to particular types of analyses; the ease of doing both without writing code is another advantage of SAS Studio.
The book includes nice examples of writing your own code to complement task-generated code, which is something that traditional SAS programmers will want to do. One related feature of SAS Studio that Cody doesn't mention is the ability to save and access snippets of code that you find yourself writing frequently or code that is a little bit too obscure to recall accurately.
Kathy Roggenkamp
Research Instructor and Manager of Statistical Computing
Collaborative Studies Coordinating Center
Department of Biostatistics, UNC-Chapel Hill