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Statistical Computing Environments for Social Research
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Statistical Computing Environments for Social Research

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September 1996 | 256 pages | SAGE Publications, Inc
The nature of statistics has changed from classical notions of hypothesis testing toward graphical and exploratory data analysis that exploits the flexibility of interactive computing and graphical displays. With contributions from some of the leading researchers in the social sciences and statistics, Statistical Computing Environments for Social Research describes seven statistical computing environments--APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS/IML, and Stata--that can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to a standard statistical package, provide programming tools for building other statistical applications. Programmability, flexible data structures, and--in the case of some of the computing environments--graphical interfaces and object-oriented programming permit researchers to take advantage of emerging statistical methodologies. Three additional chapters, describing the Axis, R-code, and ViSta statistical packages, demonstrate how researchers have extended one of the computing environments--Lisp-Stat--to produce significant statistical applications employing graphical interfaces to statistical software. To illustrate the capabilities of the seven statistical computing environments, each contributor uses the same data set to perform three computing tasks: robust regression, bootstrap resampling, and kernel-density estimation. The same data are analyzed in the chapters on Axis, R-code, and ViSta packages. The chapters in Statistical Computing Environments for Social Research illustrate important ideas and techniques in modern data analysis and statistical computing, ideas and techniques that readers will be able to apply in the more effective analysis of their own data.

Robert Stine and John Fox
Editors' Introduction
 
PART ONE: COMPUTING ENVIRONMENTS
John Fox and Michael Friendly
Data Analysis Using APL2 and APL2STAT
J Scott Long and Brian Noss
Data Analysis Using GAUSS and Markov
Luke Tierney
Data Analysis Using Lisp-Stat
Robert Stine
Data Analysis Using Mathematica
Charles Hallahan
Data Analysis Using SAS
Lawrence C Hamilton and Joseph M Hilbe
Data Analysis Using Stata
Daniel A Schulman, Alec D Campbell, and Eric C Kostello
Data Analysis Using S-Plus
 
PART TWO: EXTENDING LISP-STAT
Robert Stine
AXIS
An Extensible Graphical User Interface for Statistics

 
Sanford Weisberg
The R-Code
A Graphical Paradigm for Regression Analysis

 
Forrest W Young and Carla M Bann
ViSta
A Visual Statistics System

 

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