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Statistical Computing Environments for Social Research

by Robert A. Stine

Statistical Computing Environments for Social Research Synopsis

The nature of statistics has changed from classical notions of hypothesis testing, towards graphical and exploratory data analysis which exploits the flexibility of interactive computing and graphical displays. This book describes seven statistical computing environments - APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS/IML, and Stata - which can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to standard statistical packages, 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.

Book Information

ISBN: 9780761902690
Publication date: 21st October 1996
Author: Robert A. Stine
Publisher: SAGE Publications Inc
Format: Hardback
Pagination: 256 pages
Categories: Psychology, Social research & statistics,

About Robert A. Stine

John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). ...

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