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William G. Jacoby

Statistical Graphics for Visualizing Multivariate Data, Volume 120

Buch

This book explores a variety of graphical displays that are useful for visualizing multivariate data. The basic problem involves representing information that varies along several dimensions when the display medium (a computer screen or printed page) is inherently two-dimensional. In order to address this problem, William G Jacoby introduces the concept of a `data space'.

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Produktdetails


Weitere Autoren: Lewis-Beck, Michael S. (Solist)
  • ISBN: 978-0-7619-0899-9
  • EAN: 9780761908999
  • Produktnummer: 1408855
  • Verlag: Sage Pubn
  • Sprache: Englisch
  • Erscheinungsjahr: 1998
  • Seitenangabe: 112 S.
  • Masse: H20.3 cm x B15.2 cm x D0.7 cm 136 g
  • Auflage: 2. A.
  • Reihenbandnummer: 120
  • Gewicht: 136

Über den Autor


William G. Jacoby is a Professor in the Department of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan, where he serves as Director of the Inter-University Consortium for Political and Social Research (ICPSR) Summer Training Program in Quantitative Methods of Social Research.Professor Jacoby joined the MSU faculty in 2003. Previously, he held positions at the University of South Carolina, Ohio State University, and the University of Missouri. He received his Ph.D. from the University of North Carolina, Chapel Hill in 1983.Professor Jacoby's main professional interests are mass political behavior (public opinion, political attitudes, voting behavior) and quantitative methodology (measurement theory, scaling methods, statistical graphics, modern regression). His current research focuses on citizen ideology and belief system organization, value choices and their implications for subsequent political orientations, measuring policy priorities in the American states, the implications of measurement assumptions for statistical models, and graphical strategies for data analysis.Recently, Professor Jacoby has taught courses on public opinion, regression analysis, scaling methods, and statistical graphics.

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