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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 res
List of contents
Editors¿ Introduction - Robert Stine and John Fox
PART ONE: COMPUTING ENVIRONMENTS
Data Analysis Using APL2 and APL2STAT - John Fox and Michael Friendly
Data Analysis Using GAUSS and Markov - J Scott Long and Brian Noss
Data Analysis Using Lisp-Stat - Luke Tierney
Data Analysis Using Mathematica - Robert Stine
Data Analysis Using SAS - Charles Hallahan
Data Analysis Using Stata - Lawrence C Hamilton and Joseph M Hilbe
Data Analysis Using S-Plus - Daniel A Schulman, Alec D Campbell, and Eric C Kostello
PART TWO: EXTENDING LISP-STAT
AXIS - Robert Stine
An Extensible Graphical User Interface for Statistics
The R-Code - Sanford Weisberg
A Graphical Paradigm for Regression Analysis
ViSta - Forrest W Young and Carla M Bann
A Visual Statistics System
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.
Summary
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 res
