Using SAS for Data Management, Statistical Analysis, and Graphics: 1st Edition (Paperback) book cover

Using SAS for Data Management, Statistical Analysis, and Graphics

1st Edition

By Ken Kleinman, Nicholas J. Horton

CRC Press

305 pages | 32 B/W Illus.

Purchasing Options:$ = USD
Paperback: 9781439827574
pub: 2010-07-28
Hardback: 9781138469846
pub: 2017-11-15

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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.


This book is a well-organized reference text that summarizes and illustrates SAS code and common SAS features most often used by statistical analysts and others engaged in research and data analysis. … a handy reference tool for common tasks performed in SAS due to the book’s task-oriented nature and the broad range of topics covered. This book would also nicely serve as a supplemental reference text for an introductory SAS programming class.

Journal of Biopharmaceutical Statistics, Issue 3, 2011

Table of Contents

Introduction to SAS


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


Data Management



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


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


Subject Index

SAS Index

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General