"My clients rely on me to do things right, and want to be assured of this. I anticipate referencing this book frequently in my SAS programs and project documentation because it covers so much of what's routinely done in data analysis that nevertheless must be communicated to non-programmers and/or non-statisticians. Elementary Statistics Using SASŪ is a genuine confidence booster for knowing I'm using the correct method for the data at hand. Be it summarizing data, providing estimates of the mean, dealing with confidence intervals, evaluating effects of sample size, comparing groups of data or basic regression, it's covered in this book.
Moreover I have ready access to the theoretical information I like to have on hand to have a solid understanding of what I am doing and why. If I have to explain it to someone else, my job is now made easy. Previously I would have hesitated to teach a class covering this material, but if I could use this book as the required text I'd likely jump at the first opportunity. I wish this book had in fact been MY introduction to statistics, and you are very fortunate if it is for you. But no worries, there was plenty new for me to learn as the scope of 'elementary' is surprisingly extensive! The author goes from the basics to multiple regression without confusing the novice and without alienating the experienced. I'd be surprised if even the savviest SAS user or statistician came away empty-handed from this book, as even one of the many often-overlooked features covered within might be an immense time-saver in a day's routine work.
The book could not be better organized. You can find what you need using the Table of Contents or the Index without much ado. I'm a big fan of step-by-step instructions, and I love that these are included but not overused. Up-to-date SAS 9.2 syntax (with notes for previous versions) follows the actual SAS documentation-a feature that facilitates using SAS documentation rather than confusing readers with attempts to replace, condense, or improve upon it. Examples are provided with complete code, and there is straightforward documentation and explanation of ODS that is integrated throughout. If you desire to output results to a table, you will find what you need to know, where you expect to find it. Everything presented is relevant to task and a variety of real, rather than contrived, data are featured. There is very little, if any, 'fluff' in this book.
No matter how complex your final model is, you always need to start at the basics with your data. If you, as a senior statistician or manager, are not doing these preliminary analyses and data investigations yourself, I predict you'll want to after seeing in this book what 9.2 has to offer. Ms. Schlotzhauer puts the enhanced graphics of SAS STAT 9.2 through its paces, showcasing its capabilities and pointing out many practical aspects of employing routinely what was formerly considered somewhat elusive, nice-to-have-if-you-can-get-it output. Rather than stop at these visually appealing and quick-to-produce graphs, the author provides guidance on putting them to good use: the hows and whys of investigating outliers and missing data, testing assumptions, evaluating goodness of fit, and more. After reading these sections, my days of SAS graphics getting the better of me are surely over.
I own an alarming number of SAS books by users that are specific to task, and have printed out, electronically stored, or bookmarked too many user group papers to count, also for specific tasks. This book impresses me as the first one that I'll be inclined to turn to again and again and will not hesitate to recommend it to clients for a variety of reasons already mentioned. If any single SAS book should be spiral-bound to spare the binding, this is the one! I'd say that numerous pages would be well dog-eared, but it's unnecessary-SAS code in this book is available in electronic format, and I'm sure I won't be the first, or the last, to add customized macro-enabled versions to my programmer toolkit."
"This book does an excellent job of addressing three major issues where other books of this type have failed:
-Lack of statistical methods background by the SAS user
-Lack of SAS programming ability by the data analyst
-The large number of environments in which the SAS statistical software is used
At the beginning of each section, the interpretation of the statistical methods used in the section is described. These descriptions all but supplant the need for an accompanying introductory statistical textbook in order to apply and understand the results of the data analysis methods used in the book.
The book does not assume a previous knowledge of SAS programming. Early chapters in the book give a good summary of the basics of SAS DATA step programming, the creation and manipulation of SAS data sets, and specifics on the differences in inputs and outputs the data analyst will see in the different SAS operating environments (batch, noninteractive, interactive line, and display manager). The book also gives a great summary of how to import data from various sources and formats into SAS.
The author realizes the broad spectrum of environments, ranging from industry environments to academic and non-profit organizations, where data analyses using SAS are performed. For example, the book covers the use of ODS graphics, the newest innovation from SAS in the output of graphical information, but still covers the use of line printer plots for SAS installations where the graphics products are not available.
In general, the book covers all the basic statistical methods that would be used in routine data analyses. References in the back of the book are categorized by statistical topic. This is helpful if the user seeks more specific statistical information on a topic.
I would rate this book as the best attempt so far to provide a stand-alone text that provides enough statistical background to educate the reader in the application (and misapplication) of statistical methods to data while providing enough statistical programming instruction to allow the reader to perform basic data analyses in a number of programming environments."
"In this book the author first introduces you to SAS software, then shows how to use SAS to perform basic statistical analysis. The book is well written and organized. General topics include how to use the SAS windowing environment to write and submit SAS programs; how to create SAS data sets and modify the variables that they contain; basic descriptive statistics (measures of central tendency and variability, frequency tables, and graphs); correlation and regression, performing basic linear regression (lines, curves, and two-variable models); performing simple regression diagnostics (residuals plots); t tests (single-sample, independent and paired samples); analysis of variance (ANOVA), both one-way and factorial; and the chi-square test of independence. Even if you are new to statistics and computers, you soon will be recording research data, writing SAS programs, and interpreting the results."
"[This] book uses clear task-driven examples to show the need for the analysis tools discussed within, and weaves the SAS code and output into the text so that readers can learn both how to use SAS and the techniques themselves simultaneously. . . . Its simplicity makes it an excellent basic primer for its intended audience of new SAS users. The summaries of key ideas and SAS code could serve as a good reference to those who only use SAS occasionally."