Kleinman / Horton Using SAS for Data Management, Statistical Analysis, and Graphics

1. Auflage 2010
ISBN: 978-1-4398-2758-1
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 305 Seiten

Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics
A unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics.

Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website.

Helping to improve your analytical skills, this book lucidly summarizes the features of SAS most often used by statistical analysts. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.

Zielgruppe

Graduate students, researchers, and practitioners in statistics and data analysis; scientists who use SAS.

Autoren/Hrsg.

Kleinman, Ken

Horton, Nicholas J.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

Introduction to SAS
Installation
Running SAS and a sample session
Learning SAS and getting help
Fundamental structures: Data step, procedures, and global statements
Work process: The cognitive style of SAS
Useful SAS background
Accessing and controlling SAS output: The Output Delivery System
The SAS Macro Facility: Writing functions and passing values
Interfaces: Code and menus, data exploration, and data analysis
Miscellanea

Data Management
Input
Output
Structure and meta-data
Derived variables and data manipulation
Merging, combining, and subsetting datasets
Date and time variables
Interactions with the operating system
Mathematical functions
Matrix operations
Probability distributions and random number generation
Control flow, programming, and data generation
Further resources
HELP examples

Common Statistical Procedures
Summary statistics
Bivariate statistics
Contingency tables
Two sample tests for continuous variables
Further resources
HELP examples

Linear Regression and ANOVA
Model fitting
Model comparison and selection
Tests, contrasts, and linear functions of parameters
Model diagnostics
Model parameters and results
Further resources
HELP examples

Regression Generalizations and Multivariate Statistics
Generalized linear models
Models for correlated data
Further generalizations to regression models
Multivariate statistics and discriminant procedures
Further resources
HELP examples

Graphics
A compendium of useful plots
Adding elements
Options and parameters
Saving graphs
Further resources
HELP examples
Advanced Applications
Simulations and data generation
Power and sample size calculations
Sampling from a pathological distribution
Read variable format files and plot maps
Data scraping and visualization
Missing data: Multiple imputation
Further resources
Appendix: The HELP Study Dataset
Bibliography
Subject Index
SAS Index

Über Autor(innen)

Ken Kleinman is an associate professor in the Department of Population Medicine at Harvard Medical School in Boston, Massachusetts. His research deals with clustered data analysis, surveillance, and epidemiological applications in projects ranging from vaccine and bioterrorism surveillance to observational epidemiology to individual-, practice-, and community-randomized interventions.
Nicholas J. Horton is an associate professor in the Department of Mathematics and Statistics at Smith College in Northampton, Massachusetts. His research interests include longitudinal regression models and missing data methods, with applications in psychiatric epidemiology and substance abuse research.