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Regression by Eye: Estimating Trends in Bivariate Visualizations

Published: 02 May 2017 Publication History

Abstract

Observing trends and predicting future values are common tasks for viewers of bivariate data visualizations. As many charts do not explicitly include trend lines or related statistical summaries, viewers often visually estimate trends directly from a plot. How reliable are the inferences viewers draw when performing such regression by eye? Do particular visualization designs or data features bias trend perception? We present a series of crowdsourced experiments that assess the accuracy of trends estimated using regression by eye across a variety of bivariate visualizations, and examine potential sources of bias in these estimations. We find that viewers accurately estimate trends in many standard visualizations of bivariate data, but that both visual features (e.g., "within-the-bar" bias) and data features (e.g., the presence of outliers) can result in visual estimates that systematically diverge from standard least-squares regression models.

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    cover image ACM Conferences
    CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
    May 2017
    7138 pages
    ISBN:9781450346559
    DOI:10.1145/3025453
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    Published: 02 May 2017

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    Author Tags

    1. graphical perception
    2. information visualization
    3. regression

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    Cited By

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    • (2024)Effects of outlier and familiar context in trend-line estimates in scatterplotsMemory & Cognition10.3758/s13421-024-01646-0Online publication date: 21-Oct-2024
    • (2024)What We Augment When We Augment Visualizations: A Design Elicitation Study of How We Visually Express Data RelationshipsProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656666(1-5)Online publication date: 3-Jun-2024
    • (2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
    • (2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
    • (2024)Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345636131:1(1006-1016)Online publication date: 10-Sep-2024
    • (2024)Beware of Validation by Eye: Visual Validation of Linear Trends in ScatterplotsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345630531:1(787-797)Online publication date: 10-Sep-2024
    • (2024)Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential StatisticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332652130:1(230-239)Online publication date: 1-Jan-2024
    • (2024)EVM: Incorporating Model Checking into Exploratory Visual AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332651630:1(208-218)Online publication date: 1-Jan-2024
    • (2024)Complexity as Design Material Position Paper2024 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)10.1109/BELIV64461.2024.00013(71-80)Online publication date: 14-Oct-2024
    • (2024)Bars, lines and points: The effect of graph format on judgmental forecastingInternational Journal of Forecasting10.1016/j.ijforecast.2022.11.00340:1(44-61)Online publication date: Jan-2024
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