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Full Description
Updated to reflect SAS 9.2, A Handbook of Statistical Analyses using SAS, Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS.
Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference, analysis of variance, regression, generalized linear models, longitudinal data, survival analysis, principal components analysis, factor analysis, cluster analysis, discriminant function analysis, and correspondence analysis. They demonstrate the analyses through real-world examples, including methadone maintenance treatment, the relation of cirrhosis deaths to alcohol consumption, a sociological study of children, heart transplant treatment, and crime rate determinants.
With the data sets and SAS code available online, this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.
Contents
Introduction to SAS. Data Description and Simple Inference: Mortality and Water Hardness in the United Kingdom. Simple Inference for Categorical Data: From Sandflies to Organic Particulates in the Air. Analysis of Variance I: Treating Hypertension. Analysis of Variance II: School Attendance among Australian Children. Simple Linear Regression: Alcohol Consumption and Cirrhosis Deaths and How Old Is the Universe? Multiple Regression: Determinants of the Crime Rate in States of the United States. Logistic Regression: Psychiatry Screening, Plasma Proteins, Danish Do-It-Yourself, and Lower Back Pain. Generalized Linear Models: Polyposis and School Attendance among Australian School Children. Generalized Additive Models: Burning Rubber and Air Pollution in the United States. Analysis of Variance of Repeated Measures Visual Acuity. Longitudinal Data I: Treatment of Postnatal Depression. Longitudinal Data II: Linear Mixed Models. Longitudinal Data III: Generalized Estimating Equations and Generalized Mixed Models. Survival Analysis: Gastric Cancer, the Treatment of Heroin Addicts, and Heart Transplants. Principal Components Analysis and Factor Analysis: Olympic Decathlon and Statements about Pain. Cluster Analysis: Air Pollution in the United States. Discriminant Function Analysis: Classifying Tibetan Skulls. Correspondence Analysis: Smoking and Motherhood, Sex and the Single Girl, and European Stereotypes. Appendix. References. Index.
