Der / Everitt A Handbook of Statistical Analyses using SAS

3. Auflage 2013
ISBN: 978-1-58488-785-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 392 Seiten

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.

Zielgruppe

Researchers, practitioners, and advanced undergraduate and graduate students in statistics, medicine, epidemiology, biology, and the social sciences.

Autoren/Hrsg.

Der, Geoff

Everitt, Brian S.

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

Introduction to SAS
Introduction
User Interface
SAS Language
Reading Data—The Data Step
Modifying SAS Data
Proc Step
Global Statements
SAS Graphics
ODS—The Output Delivery System
Enhancing Output
Some Tips for Preventing and Correcting Errors
Data Description and Simple Inference: Mortality and Water Hardness in the United Kingdom
Introduction
Methods of Analysis
Analysis Using SAS
Simple Inference for Categorical Data: From Sandflies to Organic Particulates in the Air
Introduction
Methods of Analysis
Analysis Using SAS
Analysis of Variance I: Treating Hypertension
Introduction
Analysis of Variance Model
Analysis Using SAS
Analysis of Variance II: School Attendance among Australian Children
Introduction
Analysis of Variance Model
Analysis Using SAS
Simple Linear Regression: Alcohol Consumption and Cirrhosis Deaths and How Old Is the Universe?
Introduction
Simple Linear Regression
Analysis Using SAS
Multiple Regression: Determinants of the Crime Rate in States of the United States
Introduction
Multiple Regression Model
Analysis Using SAS
Logistic Regression: Psychiatric Screening, Plasma Proteins, Danish Do-It-Yourself, and Lower Back Pain
Description of Data
Logistic Regression Model
Analysis Using SAS
Generalized Linear Models: Polyposis and School Attendance among Australian School Children
Description of Data
Generalized Linear Models
Analysis Using SAS
Generalized Additive Models: Burning Rubber and Air Pollution in the United States
Introduction
Scatterplots and Generalized Additive Models
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
Longitudinal Data I: Treatment of Postnatal Depression
Description of Data
Analyses of Longitudinal Data
Analysis Using SAS
Longitudinal Data II: Linear Mixed Models. Computerized Delivery of Cognitive Behavioral Therapy—Beat the Blues
Introduction
Linear Mixed Models for Longitudinal Data
Analysis Using SAS
Longitudinal Data III: Generalized Estimating Equations and Generalized Mixed Models: Treating Toenail Infection
Introduction
Methods for Analyzing Longitudinal Data Where the Response Variable Cannot Be Assumed to Have a Normal Distribution
Analysis Using SAS
Survival Analysis: Gastric Cancer, the Treatment of Heroin Addicts, and Heart Transplants
Introduction
Describing Survival Data
Cox’s Regression
Analysis Using SAS
Principal Components Analysis and Factor Analysis: Olympic Decathlon and Statements about Pain
Introduction
Principal Components Analysis and Factor Analysis
Analysis Using SAS
Cluster Analysis: Air Pollution in the United States
Introduction
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
Appendix
References
Index
Exercises appear at the end of each chapter.

Über Autor(innen)

Geoff Der works as a consulting statistician at the Medical Research Council Social and Public Health Sciences Unit in Glasgow, Scotland. His current research interests include the relationship between cognitive functioning and health, income and health, and models for longitudinal data.

In 2005, Brian S. Everitt retired from being head of the Department of Biostatistics and Computing in the Institute of Psychiatry at King’s College London, UK. Currently working on his 60th statistics book, he acts as a statistical consultant to a number of companies.