RegLine: Assisting Novices in Refining Linear Regression Models
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 |
Redaktører | Genny Tortora, Giuliana Vitiello, Marco Winckler |
Forlag | Association for Computing Machinery |
Publikationsdato | 2020 |
Artikelnummer | 30 |
Kapitel | 1-9 |
ISBN (Elektronisk) | 9781450375351 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | 2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italien Varighed: 28 sep. 2020 → 2 okt. 2020 |
Konference
Konference | 2020 International Conference on Advanced Visual Interfaces, AVI 2020 |
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Land | Italien |
By | Salerno |
Periode | 28/09/2020 → 02/10/2020 |
Sponsor | ACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB), ACM Special Interest Group on Multimedia (SIGMM), Association for Computing Machinery (ACM) |
Navn | ACM International Conference Proceeding Series |
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ID: 258326279