Handbook of Statistical Analyses Using SAS, Second Edition

Powerful software often comes, unfortunately, with an overwhelming amount of documentation. As a leading statistics software package, SAS is no exception. Its manuals comprise well over 10,000 pages and can intimidate, or at least bewilder, all but the most experienced users. A Handbook of Statistical Analyses using SAS, Second Edition comes to the rescue. Fully revised to reflect SAS Version 8.1, it gives a concise, straightforward description of how to conduct a range of statistical analyses. The authors have updated and expanded every chapter in this new edition, and have incorporated a significant amount of new material. The book now contains more graphical material, more and better data sets within each chapter, more exercises, and more statistical background for each method. Completely new topics include the following: Data description and simple inference for categorical variables Generalized linear models Longitudinal data: Two new chapters discuss simple approaches, graphs, summary measure, and random effect models Researcher or student, new user or veteran, you will welcome this self-contained guide to the latest version of SAS. With its clear examples and numerous exercises, A Handbook of Statistical Analyses using SAS, Second Edition is not only a valuable reference, but also forms the basis for introductory courses on either SAS or applied statistics at any level, from undergraduate to professional.

Table of Contents:
A BRIEF INTRODUCTION TO SAS Introduction The Microsoft Windows User Interface The Editor Window The Log and Output Windows Other Menus The SAS Language All SAS Statements Must End with a Semicolon Program Steps Variable Names and Data Set Names Variable Lists The Data Step Creating SAS Data Sets from Raw Data The Data Statement The Infile Statement The Input Statement Reading Data from an Existing SAS Data Set Storing SAS Data Sets on Disk Modifying SAS Data Creating and Modifying Variables Deleting Variables Deleting Observations Subsetting Data Sets Concatenating and Merging Data Sets Merging Data Sets: Adding Variables The Operation of the Data Step The proc Step The proc Statement The var Statement The where Statement The by Statement The class Statement Global Statements ODS: The Output Delivery System SAS Graphics Proc gplot Overlaid Graphs Viewing and Printing Graphics Some Tips for Preventing and Correcting Errors DATA DESCRIPTION AND SIMPLE INFERENCE: MORTALITY AND WATER HARDNESS IN THE U.K. Description of Data Methods of Analysis Analysis Using SAS SIMPLE INFERENCE FOR CATEGORICAL DATA: FROM SANDFLIES TO ORGANIC PARTICULATES IN THE AIR Description of Data Methods of Analysis Analysis Using SAS Cross-classifying Raw Data Sandflies Acacia Ants Piston Rings Oral Contraceptives Oral Cancers Particulates and Bronchitis MULTIPLE REGRESSION: DETERMINANTS OF CRIME RATE IN THE UNITED STATES Description of Data The Multiple Regression Model Analysis Using SAS ANALYSIS OF VARIANCE I: TREATING HYPERTENSION Description of Data Analysis of Variance Model Analysis Using SAS ANALYSIS OF VARIANCE II: SCHOOL ATTENDANCE AMONGST AUSTRALIAN CHILDREN Description of Data Analysis of Variance Model Type I Sums of Squares Type III Sums of Squares Analysis Using SAS ANALYSIS OF VARIANCE OF REPEATED MEASURES: VISUAL ACUITY Description of Data Repeated Measures Data Analysis of Variance for Repeated Measures Designs Analysis Using SAS LOGISTIC REGRESSION: PSYCHIATRIC SCREENING, PLASMA PROTEINS, AND DANISH DO-IT-YOURSELF Description of Data The Logistic Regression Model Analysis Using SAS GHQ Data ESR and Plasma Levels Danish Do-It-Yourself GENERALIZED LINEAR MODELS: SCHOOL ATTENDANCE AMONGST AUSTRALIAN SCHOOL CHILDREN Description of Data Generalized Linear Models Model Selection and Measure of Fit Analysis Using SAS LONGITUDINAL DATA I: THE TREATMENT OF POSTNATAL DEPRESSION Description of Data The Analyses of Longitudinal Data Analysis Using SAS Graphical Displays Response Feature Analysis LONGITUDINAL DATA II: THE TREATMENT OF ALZHEIMER'S DISEASE Description of Data Random Effects Models Analysis Using SAS SURVIVAL ANALYSIS: GASTRIC CANCER AND METHADONE TREATMENT OF HEROIN ADDICTS Description of Data Describing Survival and Cox's Regression Model Survival Function Hazard Function Cox's Regression Analysis Using SAS Gastric Cancer Methadone Treatment of Heroin Addicts Exercises PRINCIPAL COMPONENTS ANALYSIS AND FACTOR ANALYSIS: THE OLYMPIC DECATHLON AND STATEMENTS ABOUT PAIN Description of Data Principal Components and Factor Analyses Principal Components Analysis Factor Analysis Analysis Using SAS Olympic Decathlon Statements about Pain CLUSTER ANALYSIS Description of Data Cluster Analysis Analysis Using SAS DISCRIMINANT FUNCTION ANALYSIS: CLASSIFYING TIBETAN SKULLS Description of Data Discriminant Function Analysis Analysis Using SAS CORRESPONDENCE ANALYSIS: SMOKING AND MOTHERHOOD, SEX AND THE SINGLE GIRL, AND EUROPEAN STEREOTYPES Description of Data Displaying Contingency Table Data Graphically Using Correspondence Analysis Analysis Using SAS Boyfriends Smoking and Motherhood Are the Germans Really Arrogant? Exercises APPENDICES SAS Macro to Produce Scatterplot Matrices Answers to Selected Chapter Exercises REFERENCES Each chapter also contains Exercises

Review :
"a concise but simple manual for using SAS. It has put thousands of pages of SAS manuals in less than 400 pages of a paperbackdescribes the use of SAS Version 8.1 in the Microsoft Windows Environment. However, the programming part can be used in almost any environment The authors have again done an excellent job of explaining each statement as well as the resulting output In summary, the handbook introduces SAS programming through a number of datasets of varied complexity, makes the datasets suitable for analysis through programming statements, then uses SAS procedures for analysis. SAS procedures and the resulting outputs are properly explained, and suggestions for appropriate analyses are provided where necessary. Overall, I consider this an excellent handbook for a beginning SAS user. Statisticians and nonstatisticians can both benefit from this book." -The American Statistician, November 2002 "The aim of the book - to provide an easy to read reference for new users of SAS without having to resort to trawling through the manuals -- is well met. I feel that this would be a very good starting point for any student or researcher who wishes to learn the basics about the statistical techniques covered and how they can be programmed in SAS. The examples provided in the book cover a good range of data situations and analysis techniques." -M. K. Smith, Biometrics, June 2002