Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise

Daniel Reimann; Christine Blech; Nilam Ram; Robert Gaschler

doi:10.1109/TVCG.2021.3051853

Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise

Daniel Reimann, Christine Blech, Nilam Ram, Robert Gaschler

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.

Original language	English (US)
Article number	9325067
Pages (from-to)	3834-3838
Number of pages	5
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	27
Issue number	9
DOIs	https://doi.org/10.1109/TVCG.2021.3051853
State	Published - Sep 1 2021

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

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10.1109/TVCG.2021.3051853

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abstract = "Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation. ",

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Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise

Abstract

All Science Journal Classification (ASJC) codes

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