RegLine: Assisting Novices in Refining Linear Regression Models

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

RegLine : Assisting Novices in Refining Linear Regression Models. / Wang, Xiaoyi; Micallef, Luana; Hornbæk, Kasper.

Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. ed. / Genny Tortora; Giuliana Vitiello; Marco Winckler. Association for Computing Machinery, 2020. 30 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Wang, X, Micallef, L & Hornbæk, K 2020, RegLine: Assisting Novices in Refining Linear Regression Models. in G Tortora, G Vitiello & M Winckler (eds), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020., 30, Association for Computing Machinery, ACM International Conference Proceeding Series, 2020 International Conference on Advanced Visual Interfaces, AVI 2020, Salerno, Italy, 28/09/2020. https://doi.org/10.1145/3399715.3399913

APA

Wang, X., Micallef, L., & Hornbæk, K. (2020). RegLine: Assisting Novices in Refining Linear Regression Models. In G. Tortora, G. Vitiello, & M. Winckler (Eds.), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 [30] Association for Computing Machinery. ACM International Conference Proceeding Series https://doi.org/10.1145/3399715.3399913

Vancouver

Wang X, Micallef L, Hornbæk K. RegLine: Assisting Novices in Refining Linear Regression Models. In Tortora G, Vitiello G, Winckler M, editors, Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. Association for Computing Machinery. 2020. 30. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3399715.3399913

Author

Wang, Xiaoyi ; Micallef, Luana ; Hornbæk, Kasper. / RegLine : Assisting Novices in Refining Linear Regression Models. Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. editor / Genny Tortora ; Giuliana Vitiello ; Marco Winckler. Association for Computing Machinery, 2020. (ACM International Conference Proceeding Series).

Bibtex

@inproceedings{91128215d25e4c93a2a152d2d260fb96,
title = "RegLine: Assisting Novices in Refining Linear Regression Models",
abstract = "The process of verifying linear model assumptions and remedying associated violations is complex, even when dealing with simple linear regression. This process is not well supported by current tools and remains time-consuming, tedious, and error-prone. We present RegLine, a visual analytics tool supporting the iterative process of assumption verification and violation remedy for simple linear regression models. To identify the best possible model, RegLine helps novices perform data transformations, deal with extreme data points, analyze residuals, validate models by their assumptions, and compare and relate models visually. A qualitative user study indicates that these features of RegLine support the exploratory and refinement process of model building, even for those with little statistical expertise. These findings may guide visualization designs on how interactive visualizations can facilitate refining and validating more complex models. ",
keywords = "data transformation, exploratory data analysis, linear regression, model verification and remedy, residual analysis",
author = "Xiaoyi Wang and Luana Micallef and Kasper Hornb{\ae}k",
year = "2020",
doi = "10.1145/3399715.3399913",
language = "English",
series = "ACM International Conference Proceeding Series",
editor = "Genny Tortora and Giuliana Vitiello and Marco Winckler",
booktitle = "Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020",
publisher = "Association for Computing Machinery",
note = "2020 International Conference on Advanced Visual Interfaces, AVI 2020 ; Conference date: 28-09-2020 Through 02-10-2020",

}

RIS

TY - GEN

T1 - RegLine

T2 - 2020 International Conference on Advanced Visual Interfaces, AVI 2020

AU - Wang, Xiaoyi

AU - Micallef, Luana

AU - Hornbæk, Kasper

PY - 2020

Y1 - 2020

N2 - The process of verifying linear model assumptions and remedying associated violations is complex, even when dealing with simple linear regression. This process is not well supported by current tools and remains time-consuming, tedious, and error-prone. We present RegLine, a visual analytics tool supporting the iterative process of assumption verification and violation remedy for simple linear regression models. To identify the best possible model, RegLine helps novices perform data transformations, deal with extreme data points, analyze residuals, validate models by their assumptions, and compare and relate models visually. A qualitative user study indicates that these features of RegLine support the exploratory and refinement process of model building, even for those with little statistical expertise. These findings may guide visualization designs on how interactive visualizations can facilitate refining and validating more complex models.

AB - The process of verifying linear model assumptions and remedying associated violations is complex, even when dealing with simple linear regression. This process is not well supported by current tools and remains time-consuming, tedious, and error-prone. We present RegLine, a visual analytics tool supporting the iterative process of assumption verification and violation remedy for simple linear regression models. To identify the best possible model, RegLine helps novices perform data transformations, deal with extreme data points, analyze residuals, validate models by their assumptions, and compare and relate models visually. A qualitative user study indicates that these features of RegLine support the exploratory and refinement process of model building, even for those with little statistical expertise. These findings may guide visualization designs on how interactive visualizations can facilitate refining and validating more complex models.

KW - data transformation

KW - exploratory data analysis

KW - linear regression

KW - model verification and remedy

KW - residual analysis

UR - http://www.scopus.com/inward/record.url?scp=85093077278&partnerID=8YFLogxK

U2 - 10.1145/3399715.3399913

DO - 10.1145/3399715.3399913

M3 - Article in proceedings

AN - SCOPUS:85093077278

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020

A2 - Tortora, Genny

A2 - Vitiello, Giuliana

A2 - Winckler, Marco

PB - Association for Computing Machinery

Y2 - 28 September 2020 through 2 October 2020

ER -

ID: 258326279