Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models

Daniel Reimann; Nilam Ram; Robert Gaschler

doi:10.1109/TVCG.2022.3158093

Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models

Daniel Reimann
, Nilam Ram
, Robert Gaschler

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

2 Scopus citations

Abstract

Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ('lollipops') supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).

Original language	English (US)
Pages (from-to)	3436-3440
Number of pages	5
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	29
Issue number	7
DOIs	https://doi.org/10.1109/TVCG.2022.3158093
State	Published - Jul 1 2023

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/TVCG.2022.3158093

Cite this

@article{f7996b5a4bb549e5b268300f3d83d9d4,

title = "Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models",

abstract = "Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ('lollipops') supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).",

author = "Daniel Reimann and Nilam Ram and Robert Gaschler",

note = "Publisher Copyright: {\textcopyright} 1995-2012 IEEE.",

year = "2023",

month = jul,

day = "1",

doi = "10.1109/TVCG.2022.3158093",

language = "English (US)",

volume = "29",

pages = "3436--3440",

journal = "IEEE Transactions on Visualization and Computer Graphics",

issn = "1077-2626",

publisher = "IEEE Computer Society",

number = "7",

}

TY - JOUR

T1 - Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models

AU - Reimann, Daniel

AU - Ram, Nilam

AU - Gaschler, Robert

PY - 2023/7/1

Y1 - 2023/7/1

N2 - Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ('lollipops') supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).

AB - Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ('lollipops') supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).

UR - https://www.scopus.com/pages/publications/85126306203

UR - https://www.scopus.com/pages/publications/85126306203#tab=citedBy

U2 - 10.1109/TVCG.2022.3158093

DO - 10.1109/TVCG.2022.3158093

M3 - Article

C2 - 35263256

AN - SCOPUS:85126306203

SN - 1077-2626

VL - 29

SP - 3436

EP - 3440

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

IS - 7

ER -

Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots With Nonlinear Models

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this