Statistical Computing Environments for Social Research

Stine, Robert|Fox, John

ISBN 10: 0761902694 / ISBN 13: 9780761902690

Published by SAGE PUBN, 1996

About this Item

Seller Inventory # 594966727

Bibliographic Details

• Title: Statistical Computing Environments for Social Research
• Publisher: SAGE PUBN
• Publication Date: 1996
• Language: English
• ISBN 10: 0761902694
• ISBN 13: 9780761902690
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

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). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.