Graphical Assessment of Dependence: Is a Picture Worth 100 Tests?

Nonparametric tests of association have been around for a long time, and new ones continue to be proposed to cope with specific forms of association. However, graphs have the potential to assess a far richer class of bivariate dependence structures than any collection of tests. This article describe...

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Veröffentlicht in:	The American statistician 2001-08, Vol.55 (3), p.233-239
Hauptverfasser:	Fisher, N. I, Switzer, P
Format:	Artikel
Sprache:	eng
Schlagworte:	Chi-plot Copula Correlations Data sampling Datasets Exact sciences and technology Gaussian distributions Graphs Mathematical monotonicity Mathematics Multivariate analysis Nonparametric association Probability and statistics Rank tests Rank tests of independence Sampling distributions Sciences and techniques of general use Statistical analysis Statistical Computing and Graphics Statistical methods Statistical tests Statistics
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Zusammenfassung:	Nonparametric tests of association have been around for a long time, and new ones continue to be proposed to cope with specific forms of association. However, graphs have the potential to assess a far richer class of bivariate dependence structures than any collection of tests. This article describes how chi-plots, used in conjunction with the usual scatterplot, provides a useful practical tool in this regard.
ISSN:	0003-1305 1537-2731
DOI:	10.1198/000313001317098248