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Judging correlation from scatterplots and parallel coordinate plots

机译：从散点图和平行坐标图判断相关性

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摘要

Scatterplots and parallel coordinate plots (PCPs) that can both be used to assess correlation visually. In this paper, we compare these two visualization methods in a controlled user experiment. More specifically, 25 participants were asked to report observed correlation as a function of the sample correlation under varying conditions of visualization method, sample size and observation time. A statistical model is proposed to describe the correlation judgment process. The accuracy and the bias in the judgments in different conditions are established by interpreting the parameters in this model. A discriminability index is proposed to characterize the performance accuracy in each experimental condition. Moreover, a statistical test is applied to derive whether or not the human sensation scale differs from a theoretically optimal (that is, unbiased) judgment scale. Based on these analyses, we conclude that users can reliably distinguish twice as many different correlation levels when using scatterplots as when using PCPs. We also find that there is a bias towards reporting negative correlations when using PCPs. Therefore, we conclude that scatterplots are more effective than parallel plots in supporting visual correlation analysis.
机译：散点图和平行坐标图（PCP）都可以用来直观地评估相关性。在本文中，我们在受控用户实验中比较了这两种可视化方法。更具体地说，要求25位参与者报告在不同的可视化方法，样品大小和观察时间条件下，观察到的相关性与样品相关性的关系。提出了一种统计模型来描述相关性判断过程。通过解释该模型中的参数，可以确定不同条件下判断的准确性和偏差。提出了可辨别性指标来表征每种实验条件下的性能准确性。此外，应用统计检验得出人的感觉量表是否与理论上最佳（即无偏见）的判断量表不同。基于这些分析，我们得出结论，使用散点图时，用户可以可靠地区分两倍的不同相关级别，而使用PCP时，则可以。我们还发现使用PCP时报告报告负相关性存在偏差。因此，我们得出结论，在支持视觉相关性分析方面，散点图比平行图更有效。

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