Visualization of Univariate Data for Comparison

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Authors Csaba Faragó
Journal/Conference Name Annales Mathematicae et Informaticae
Paper Category
Paper Abstract “A picture is worth a thousand words.” This idiom is true for research studies as well: illustrations in a paper helps the reader to better understand the findings of the authors. There are already several possibilities for visualizing data. But there always exist cases when the currently available diagram types are not useful enough. We also ran into such a situation, and created two new diagram types: Cumulative Characteristic Diagram and Quantile Dierence Diagram for illustrating data sets of numeric types. The Cumulative Characteristic Diagram is a curve, which is based on the non-ascending order of the values. It makes it easy to read many characteristics of the input data, and it is suitable to find similarities and dierences between several data sets quickly. Quantile Dierence Diagram draws the dierences of two ascending sets of data on the same quantiles. This diagram is suitable to illustrate in which subset the data are higher, and it also reveals some important details, which would remain hidden using statistic tests only. We found them very useful both in explaining our actual results, and gaining ideas for further development directions. In this article we show the usefulness of these diagrams illustrating the results of Contingency ChiSquared tests, Wilcoxon rank tests and variance tests.
Date of publication 2015
Code Programming Language R
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