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

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

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.