Home » Using SAS for Data Management, Statistical Analysis, and Graphics
Product Image

Using SAS for Data Management, Statistical Analysis, and Graphics

Ken Kleinman | Nicholas J. Horton
€88.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Ken Kleinman
A01=Nicholas J. Horton
advanced SAS programming examples
Author_Ken Kleinman
Author_Nicholas J. Horton
Base SAS
case study analysis
Category=PBT
Category=PS
creation of graphics
data cleaning techniques
Data Ds
data management
data visualization
descriptive summaries
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
GLM Procedure
gplot
inferential procedures
inferential statistics
mixed
multivariate analysis
multivariate methods
ods
Ods Graphics
Ods Output Parameterestimates
options
print
proc
Proc Freq Data
Proc Genmod
Proc Genmod Data
Proc Glm Data
Proc Gplot Data
Proc Iml
Proc Logistic Data
Proc Mixed Data
Proc Print Data
Proc Reg Data
Proc Sgplot
Proc Sort Data
Proc Summary Data
Proc Ttest Data
Proc Univariate Data
products
reg
regression analysis
regression modeling
SAS
SAS Dataset
SAS ODS
SAS Procedure
SAS Product
simulations
sort
statistical analysis
statistical computing
statistical models
statistical tests
SUBSTANCE Cocaine
SUBSTANCE Heroin
univariate

Product details

  • ISBN 9781439827574
  • Weight: 2840g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 Jul 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns
Description

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.
About Ken Kleinman | Nicholas J. Horton
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.

More from this author