Synopses & Reviews
Synopsis
Designed for researchers primarily interested in what their data are revealing, Applied Regression and ANOVA Using SAS presents rigorous statistical methods without burdening readers with matrix algebra and calculus. Each method is introduced by discussing its characteristic features, the reasons readers would want to use it, and its underlying assumptions. The book then guides readers in applying each method by describing a step-by-step approach, giving SAS code to implement the steps. The SAS code is annotated, which allows users to readily adapt it to their own data set.
In the step-by-step approach, readers are given practical advice on how to evaluate in depth whether the assumptions of a method are reasonable for their data set. The book also gives practical advice on interpreting results in the light of modern multiple testing procedures and simultaneous confidence intervals.
Readers are shown throughout the book how high resolution, publication-ready graphics associated with regression and ANOVA methods are produced with virtually no effort by the SAS user. Suggestions for navigating issues encountered in analyzing real-life data make this book invaluable to both non-statisticians and applied statisticians.
Synopsis
Applied Regression and ANOVA Using SAS(R) has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps.
Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided.
Features:
-Statistical concepts presented in words without matrix algebra and calculus
-Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics
-Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection
-Suggestions of alternative approaches when a method's ideal inference conditions are unreasonable for one's data
This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.