Visualizing data, visualizing models: getting priorities right when analyzing data

• Authors: Alex Chavez , Richard Gonzalez

Abstracts

EN

We discuss methods of visualizing data and statistical models. The key innovation of these methods is that specialized graphs simultaneously illustrate underlying patterns in data and the properties of the statistical model that are used to analyze the data. These visualization techniques provide ways of understanding the relation between variables, illustrate how the statistical model works and can reveal how the statistical model may not be capturing underlying patterns among the variables.

• Publisher: Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
• Journal: Problemy Zarządzania
• Year: 2009
• Volume: 7
• Issue: 4(26)
• Pages: 185-195

Dates

published

2009

Contributors

author

Alex Chavez

• Department of Psychology, University of Michigan, USA

author

Richard Gonzalez

• Department of Psychology, University of Michigan, USA

References

• Chambers, J.M. 1992. Data for models. In Statistical Modeb, eds. J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole.
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• Cleveland, W.S. 1993. Visualizing Data, New Jersey: Summit Press.
• Friendly, M. 2002. Corrgrams: Exploratory displays for correlation matrices. American Statistician, Vol. 56, p. 316-324.
• Lang, D.T. & Swayne, D.F. 2001. GGobi meets R: an extensible environment for interactive dynamic data visualization. Proceedings of the 2nd International Workshop on Distributed Statistical Computing.
• Sarkar, D. 2009. Lattice: Multivariate data visualization with R, Springer.

Identifiers

Biblioteka Nauki

49389359