SAS Companion for Nonparametric Statistics

Need a guide to using SAS to carry out non-parametric analysis? SAS COMPANION FOR NONPARAMETRIC STATISTICS provides an excellent knowlege base and provides examples you can use to practice using the program. All SAS examples presented are self-contained and can be entered into SAS as they appear, and executed. Thus, you don't have to deal with issues of creating SAS data sets before using the programs. In addition to presenting the SAS code to obtain various nonparametric analyses, brief introductions to the methods themselves are provided. Particular attention is given to how SAS calculates the results it presents.

SAS is probably the most widely used statistical analysis software, and includes a number of tools to carry out nonparametric analyses. This guide, which includes a range of examples, covers one-sample and two-sample methods, K-sample methods, paired comparisons and blocked designs, tests of association for bivariate data and contingency tables, analysis of censored data, multivariate permutation tests, smoothing methods and robust model fitting. Annotation ©2005 Book News, Inc., Portland, OR

Scott J. Richter is Assistant Professor of Statistics at University of North Carolina at Greensboro. He is the author of the online SAS Lab Manual for INTRODUCTION TO MODERN NONPARAMETRIC STATISTICS the Duxbury textbook by James J. Higgins, and has published several articles in the area of robust statistical methods. He is also Director of the Statistical Consulting Center at UNCG. His primary research interest is in the area of robust statistical methods.

James J. Higgins is Professor of Statistics at Kansas State University and Fellow of the American Statistical Association. He is the co-author of the Duxbury textbook CONCEPTS IN PROBABILITY AND STOCHASTIC MODELING with Sallie Keller-McNulty and he is author of INTRODUCTION TO MODERN NONPARAMETRIC STATISTICS as well as having over 80 scientific publications to his credit. In addition, he is a statistical consultant for Kansas State Research and Extension. His research interests include nonparametric statistics and reliability theory.