This book is an integrated treatment of applied statistical methods, presented at an intermediate level, and the SAS programming language. It serves as an advanced introduction to SAS as well as how to use SAS for the analysis of data arising from many different experimental and observational studies. While there are many introductory texts on SAS programming, statistical methods texts that solely make use of SAS as the software of choice for the analysis of data are rare. While this is understandable from a marketability point of view, clearly such texts will serve the need of many thousands of students and professionals who desire to learn how to use SAS beyond the basic introduction they usually receive from taking an introductory statistics course. More recently, several authors in statistical methodology have begun to incorporate SAS in their texts but these books are limited to more specialized subjects. Many of the standard topics covered in statistical methods texts supplemented by advanced material more suited for a second course in applied statistics are included, so that specific aspects of SAS procedures can be illustrated. Brief but instructive reviews of the statistical methodologies used are provided, and then illustrated with analysis of data sets used in well-known statistical methods texts. Particular attention is devoted to discussions of models used in each analysis because the authors believe that it is important for users to have not only an understanding of how these models are represented in SAS but also because it helps in the interpretation of the SAS output produced. Mervyn G. Marasinghe is Associate Professor of Statistics at Iowa State University where he teaches several courses in statistics and statistical computing and a course in data analysis using SAS software. A former Associate Editor of the Journal Computational and Graphical Statistics, he has used SAS software for more than 30 years. William J. Kennedy is Professor Emeritus of Statistics at Iowa State University. A Fellow of the American Statistical Association and former Editor of The American Statistician and Journal of Computational and Graphical Statistics, he is coauthor of the book entitled Statistical Computing.