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Journal articles on the topic 'Statistical visualizing'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

de Levie, Robert. "Visualizing Statistical Concepts." Journal of Chemical Education 85, no. 5 (2008): 635. http://dx.doi.org/10.1021/ed085p635.1.

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Allen, Genevera I., Frederick Campbell, and Yue Hu. "Comments on “visualizing statistical models”: Visualizing modern statistical methods for Big Data." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 226–28. http://dx.doi.org/10.1002/sam.11272.

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3

Katsaounis, Tena I. "Visualizing Statistical Models and Concepts." Technometrics 45, no. 2 (2003): 175–76. http://dx.doi.org/10.1198/tech.2003.s138.

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4

Williams, Brian J. "Visualizing Statistical Models and Concepts." Journal of the American Statistical Association 99, no. 465 (2004): 292. http://dx.doi.org/10.1198/jasa.2004.s315.

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5

Pfahler, Lori B., and William G. Jacoby. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 3 (1999): 262. http://dx.doi.org/10.2307/1270570.

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Holmes, Susan, and William G. Jacoby. "Statistical Graphics for Visualizing Multivariate Data." Journal of the American Statistical Association 94, no. 447 (1999): 981. http://dx.doi.org/10.2307/2670014.

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Pfahler, Lori B. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 3 (1999): 261–62. http://dx.doi.org/10.1080/00401706.1999.10485674.

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8

Heckler, Charles E. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 4 (1999): 369. http://dx.doi.org/10.1080/00401706.1999.10485938.

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9

Wickham, Hadley, Dianne Cook, and Heike Hofmann. "Visualizing statistical models: Removing the blindfold." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 203–25. http://dx.doi.org/10.1002/sam.11271.

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Braşoveanu, Adrian M. P., Marta Sabou, Arno Scharl, Alexander Hubmann-Haidvogel, and Daniel Fischl. "Visualizing statistical linked knowledge for decision support." Semantic Web 8, no. 1 (2016): 113–37. http://dx.doi.org/10.3233/sw-160225.

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11

Armstrong, Zan, and Martin Wattenberg. "Visualizing Statistical Mix Effects and Simpson's Paradox." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2132–41. http://dx.doi.org/10.1109/tvcg.2014.2346297.

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12

Kim, Sung Hwa, Jihye Lim, and Dae Ryong Kang. "Statistical Methods for Visualizing Healthcare Big Data." Journal of Health Informatics and Statistics 48, Suppl 2 (2023): S23—S33. http://dx.doi.org/10.21032/jhis.2023.48.s2.s23.

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With the advancement and acceleration of digital technology, the demand and supply of healthcare big data are increasing. In Korea, the government and companies have made various efforts to utilize healthcare big data, such as deregulation data-related legal regulations and data linkage between different institutions. As a result, many researchers have been able to access a variety of healthcare big data. Although healthcare big data has a vast amount and high value, many researchers are unable to fully access healthcare big data because there are difficulties in processing, analysis, and inte

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13

Daiga, Michael, and Shannon Driskell. "Visualizing the Arithmetic Mean." Mathematics Teacher: Learning and Teaching PK-12 114, no. 8 (2021): 607–15. http://dx.doi.org/10.5951/mtlt.2020.0192.

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The two provided activities are geared for students in middle school to facilitate and deepen their understanding of the arithmetic mean. Through these activities, students analyze visual representations and use a special type of statistical thinking called transnumerative thinking.

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Lüdecke, Daniel, Indrajeet Patil, Mattan Ben-Shachar, Brenton Wiernik, Philip Waggoner, and Dominique Makowski. "see: An R Package for Visualizing Statistical Models." Journal of Open Source Software 6, no. 64 (2021): 3393. http://dx.doi.org/10.21105/joss.03393.

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15

Erofeev, Ermak, and Irina Kulakova. "DEVELOPMENT OF A TOOL FOR VISUALIZING STATISTICAL DATA." Scientific Papers Collection of the Angarsk State Technical University 2024, no. 1 (2024): 42–46. http://dx.doi.org/10.36629/2686-7788-2024-1-42-46.

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The article analyzes possibilities of the tool horizontal swing diagram for visualization of statistical data, develops an algorithm of construction of a tool with specified properties, implements this algorithm

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Young, Forrest W. "Visualizing Six-Dimensional Structure with Dynamic Statistical Graphics." CHANCE 2, no. 1 (1989): 22–30. http://dx.doi.org/10.1080/09332480.1989.11882321.

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17

Hurley, Catherine B. "Discussion of “visualizing statistical models: Removing the blindfold”." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 229–31. http://dx.doi.org/10.1002/sam.11273.

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Patil, Prasad, and Jeffrey T. Leek. "Discussion of “visualizing statistical models: Removing the blindfold”." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 240–41. http://dx.doi.org/10.1002/sam.11275.

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19

Avron, J. E., G. Bisker, and O. Kenneth. "Visualizing two qubits." Journal of Mathematical Physics 48, no. 10 (2007): 102107. http://dx.doi.org/10.1063/1.2795217.

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20

Chaney, Allison, and David Blei. "Visualizing Topic Models." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 419–22. http://dx.doi.org/10.1609/icwsm.v6i1.14321.

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Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our

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21

Keim, Daniel A., Stephen C. North, Christian Panse, and Jörn Schneidewind. "Visualizing Geographic Information: VisualPoints vs CartoDraw." Information Visualization 2, no. 1 (2003): 58–67. http://dx.doi.org/10.1057/palgrave.ivs.9500039.

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Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this paper, we deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms that solve the continuous cartogram problem. The first one uses an iterative relocation of vertices based on scanlines. This algorithm explicitly accounts

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22

Benjamin Zhan, Feibing, and Barbara P. Buttenfield. "Object-oriented knowledge-based symbol selection for visualizing statistical information." International journal of geographical information systems 9, no. 3 (1995): 293–315. http://dx.doi.org/10.1080/02693799508902038.

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23

Höfler, Michael, Tanja Brückl, Antje Bittner, and Roselind Lieb. "Visualizing Multivariate Dependencies with Association Chain Graphs." Methodology 3, no. 1 (2007): 24–34. http://dx.doi.org/10.1027/1614-2241.3.1.24.

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In a recent paper, a new type of graph to visualize the results from graphical models was proposed. Association chain graphs (ACGs) provide a richer visualization than conventional graphs (directed acyclic and recursive regression graphs) if the data can be described with only a small number of parameters. ACGs display not only which associations reach statistical significance, but also the magnitude of associations (confidence intervals for statistical main effects) as the contrast color to the background color of the graph. In this paper, the ACG visualization is extended especially for the

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24

Cao, Nan, Yu-Ru Lin, David Gotz, and Fan Du. "Z-Glyph: Visualizing outliers in multivariate data." Information Visualization 17, no. 1 (2017): 22–40. http://dx.doi.org/10.1177/1473871616686635.

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Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, “Z-Glyph,” a family of glyphs designed to facilitate human judgment in outlier analysis of multivariate data. By employing a location-scale transformation, a Z-Glyph represents the “normal” data using regular shapes (e

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25

Cacciafesta, Fabrizio. "Visualizing the Variance of a Random Variable." Open Systems & Information Dynamics 18, no. 01 (2011): 71–85. http://dx.doi.org/10.1142/s1230161211000054.

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We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

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26

Hand, David J. "Visualizing Time: Designing Graphical Representations for Statistical Data by Graham Wills." International Statistical Review 80, no. 3 (2012): 482–83. http://dx.doi.org/10.1111/j.1751-5823.2012.00196_14.x.

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27

Xu, Jason, Yiwen Wang, Peter Guttorp, and Janis L. Abkowitz. "Visualizing hematopoiesis as a stochastic process." Blood Advances 2, no. 20 (2018): 2637–45. http://dx.doi.org/10.1182/bloodadvances.2018023705.

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Abstract Stochastic simulation has played an important role in understanding hematopoiesis, but implementing and interpreting mathematical models requires a strong statistical background, often preventing their use by many clinical and translational researchers. Here, we introduce a user-friendly graphical interface with capabilities for visualizing hematopoiesis as a stochastic process, applicable to a variety of mammal systems and experimental designs. We describe the visualization tool and underlying mathematical model, and then use this to simulate serial transplantations in mice, human co

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28

Selinger, Robin L. B., Jonathan V. Selinger, Anthony P. Malanoski, and Joel M. Schnur. "Visualizing chiral self-assembly." Chaos: An Interdisciplinary Journal of Nonlinear Science 14, no. 4 (2004): S3. http://dx.doi.org/10.1063/1.1821692.

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29

Böttcher, Lucas, and Gregory Wheeler. "Visualizing high-dimensional loss landscapes with Hessian directions." Journal of Statistical Mechanics: Theory and Experiment 2024, no. 2 (2024): 023401. http://dx.doi.org/10.1088/1742-5468/ad13fc.

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Abstract Analyzing the geometric properties of high-dimensional loss functions, such as local curvature and the existence of other optima around a certain point in loss space, can help provide a better understanding of the interplay between neural-network structure, implementation attributes, and learning performance. In this paper, we combine concepts from high-dimensional probability and differential geometry to study how curvature properties in lower-dimensional loss representations depend on those in the original loss space. We show that saddle points in the original space are rarely corre

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30

Grau, Jan, Martin Nettling, and Jens Keilwagen. "DepLogo: visualizing sequence dependencies in R." Bioinformatics 35, no. 22 (2019): 4812–14. http://dx.doi.org/10.1093/bioinformatics/btz507.

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Abstract Summary Statistical dependencies are present in a variety of sequence data, but are not discernible from traditional sequence logos. Here, we present the R package DepLogo for visualizing inter-position dependencies in aligned sequence data as dependency logos. Dependency logos make dependency structures, which correspond to regular co-occurrences of symbols at dependent positions, visually perceptible. To this end, sequences are partitioned based on their symbols at highly dependent positions as measured by mutual information, and each partition obtains its own visual representation.

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31

Tsorlini, Angeliki, René Sieber, Lorenz Hurni, Hubert Klauser, and Thomas Gloor. "Designing a Rule-based Wizard for Visualizing Statistical Data on Thematic Maps." Cartographic Perspectives, no. 86 (July 27, 2017): 5–23. http://dx.doi.org/10.14714/cp86.1392.

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Thematic maps are used in a wide range of scientific fields to illustrate specific geographic phenomena. For their correct construction, the mapmaker has to select the appropriate data, and then consider different parameters and constraints in order to visualize them effectively. In this paper, these parameters were analyzed, so that a consistent and standardized workflow for producing thematic maps could be set up. This workflow served as the basis for designing and implementing a step-by-step wizard-based application. Its goal is to guide mapmakers—experts or laypersons—to create cartographi

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32

Coleman, William F. "Reply to Visualizing Statistical Concepts: Response from the JCE WebWare Feature Editor." Journal of Chemical Education 85, no. 5 (2008): 637. http://dx.doi.org/10.1021/ed085p637.

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33

Hamood, Shehab Hamid, and Farhood Chisab Raad. "Analysis of the visualizing changes in radar time series using the REACTIV method through satellite imagery." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3770–80. https://doi.org/10.11591/ijece.v12i4.pp3770-3780.

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A visualizing temporal stack of synthetic aperture radar (SAR) images are presented in this work, the method is called REACTIV, which enabled us to highlight color zones that have undergone change over the detected period of time. This work has been widely tested using Google earth engine (GEE) platform, this method depends on the hue-saturation-value (HSV) of visualizing space and supports estimation only in the time domain; the method does not support the spatial estimation. The coefficient of temporal coefficient variation is coded depending on the saturation color, of which several statist

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34

KORPIPÄÄ, PANU. "Visualizing constraint-based temporal association rules." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (2001): 401–10. http://dx.doi.org/10.1017/s0890060401155034.

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When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and gly

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35

Gonzalez, F., and C. Jung. "Visualizing the perturbation of partial integrability." Journal of Physics A: Mathematical and Theoretical 48, no. 43 (2015): 435101. http://dx.doi.org/10.1088/1751-8113/48/43/435101.

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36

Richards, L. J., J. T. Schnute, and N. Olsen. "Visualizing catch–age analysis: a case study." Canadian Journal of Fisheries and Aquatic Sciences 54, no. 7 (1997): 1646–58. http://dx.doi.org/10.1139/f97-073.

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Catch-age analysis has become a standard tool for modern stock assessment. Widely available graphical techniques can be customized to provide an intuitive framework for this complex procedure. We illustrate a graphical approach using data from a stock of Pacific ocean perch (Sebastes alutus). These data encompass typical problems in stock assessment, with missing data for some years and changes in age determination and survey methodologies midway through the time series. Preliminary visual inspection of the raw data reveals important features that must be accommodated in the model development.

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37

Machado, Liliane S., Ronei M. Moraes, and Elaine AMG Soares. "Learning by Experimentation: The Calculatora Estatística Visual." International Journal of Online and Biomedical Engineering (iJOE) 9, S8 (2013): 50. http://dx.doi.org/10.3991/ijoe.v9is8.3382.

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Learning from experimentation is the basis of the pedagogical theory called Constructivism. The system Calculadora Estatística is a Web-based tool to reinforce learning of statistical techniques, developed by the application of that theory. From a study about human-computer interaction, its newest version, the Calculadora Estatística Visual, was designed with visual programming features to provide an iconic and flowchart-based interface that allow users visualizing the steps necessary to perform a statistical analysis. It is composed by an interface that works on a statistical package to check

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38

Han, Kyung-Soo, Se-Jin Park, and Jeong-Yong Ahn. "Development of a R function for visualizing statistical information on Google static maps." Journal of the Korean Data and Information Science Society 23, no. 5 (2012): 971–81. http://dx.doi.org/10.7465/jkdi.2012.23.5.971.

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39

Villacorta, Pablo,J, and José,A Sáez. "SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R." R Journal 7, no. 2 (2015): 89. http://dx.doi.org/10.32614/rj-2015-023.

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40

Hautdidier, Baptiste. "Featured Graphic: What's in a NUTS? Visualizing Hierarchies of Europe's Administrative/Statistical Regions." Environment and Planning A: Economy and Space 43, no. 8 (2011): 1754–55. http://dx.doi.org/10.1068/a4457.

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41

Kim, Munsu, and Jiyeong Lee. "A Data Transformation Method for Visualizing the Statistical Information based on the Grid." Journal of Korea Spatial Information Society 23, no. 5 (2015): 31–40. http://dx.doi.org/10.12672/ksis.2015.23.5.031.

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42

Brini, Elisa, Solveig Topstad Borgen, and Nicolai T. Borgen. "Avoiding the eyeballing fallacy: Visualizing statistical differences between estimates using the pheatplot command." Stata Journal: Promoting communications on statistics and Stata 25, no. 1 (2025): 77–96. https://doi.org/10.1177/1536867x251322962.

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Graphical representations of coefficients and their confidence intervals are increasingly used in research presentations and publications because they are easier and quicker to read than tables. However, in coefficient plots that include several estimated coefficients, researchers often use confidence intervals to eyeball whether coefficients are statistically significant from each other, which results in an overly conservative test and increased risk of type II errors. To help avoid this eyeballing fallacy, we introduce the pheatplot postestimation command, which visualizes the statistical si

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43

Griffin, Dale, and Richard Gonzalez. "Models of dyadic social interaction." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1431 (2003): 573–81. http://dx.doi.org/10.1098/rstb.2002.1263.

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We discuss the logic of research designs for dyadic interaction and present statistical models with parameters that are tied to psychologically relevant constructs. Building on Karl Pearson's classic nineteenth–century statistical analysis of within–organism similarity, we describe several approaches to indexing dyadic interdependence and provide graphical methods for visualizing dyadic data. We also describe several statistical and conceptual solutions to the ‘levels of analytic’ problem in analysing dyadic data. These analytic strategies allow the researcher to examine and measure psychologi

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44

Setiawan, Ezra Putranda, and Heru Sukoco. "EXPLORING FIRST YEAR UNIVERSITY STUDENTS’ STATISTICAL LITERACY: A CASE ON DESCRIBING AND VISUALIZING DATA." Journal on Mathematics Education 12, no. 3 (2021): 427–48. http://dx.doi.org/10.22342/jme.12.3.13202.427-448.

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Statistical literacy, which is the ability to use statistics in daily life, is an essential skill for facing society 5.0. This study aims to explore first-year university students’ ability to properly use simple descriptive statistics and data visualization. Qualitative data were collected using a set of questions from 39 undergraduate students. Many students were able to calculate various descriptive statistics, but some of them were still unable to determine suitable statistics to describe the data clearly. Related to data visualization, many students failed to provide a meaningful chart tha

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45

Li, Xueyan, Tianyi Chen, Hanning Guo, and Huili Wang. "Visualizing Language and Aging From 2013-2022." Chinese Journal of Applied Linguistics 47, no. 4 (2024): 636–58. https://doi.org/10.1515/cjal-2024-0406.

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Abstract Declining cognitive abilities can be a concomitant of advanced age. As language is closely associated with cognitive abilities, changes in language abilities can be an important marker of changes in cognitive abilities. The current study is to review cognitive studies of language and aging by first identifying and exploring the major clusters and pivotal articles and then detecting emerging trends. Data of 3, 266 articles on language and aging from 2013 to 2022 were collected from the Web of Science Core Collection database. Adopting Document Co-citation Analysis, Freeman’s betweennes

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46

Kulakov, K. A., N. D. Moskin, A. A. Rogov, and R. V. Voronov. "SMALT Software Package as a Tool for the Study of Graph-Theoretic Models of Texts." Programmnaya Ingeneria 14, no. 6 (2023): 292–300. http://dx.doi.org/10.17587/prin.14.292-300.

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The SMALT software package (Statistical methods of analysis of literary texts) is implemented to conduct research in the field of attribution of literary texts. The article discusses new tools for storing, visualizing, comparing and searching data implemented in the system. They are implemented for text analysis using graph-theoretic models. Examples of philological studies performed using these tools are given.

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47

Ip, Edward H., Phillip Leung, and Joseph Johnson. "Interactive Profiler: An Intuitive, Web-Based Statistical Application in Visualizing Educational and Marketing Databases." Journal of Educational and Behavioral Statistics 29, no. 2 (2004): 157–75. http://dx.doi.org/10.3102/10769986029002157.

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We describe the design and implementation of a web-based statistical program—the Interactive Profiler (IP). The prototypical program, developed in Java, was motivated by the need for the general public to query against data collected from the National Assessment of Educational Progress (NAEP), a large-scale US survey of the academic state of American students. The emphasis of the program requirements is on bringing the NAEP data to a broader and not necessarily technically prepared audience. We show that the IP is an intuitive tool for visualizing students’ profiles. Because the same principle

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48

Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. "Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation." Journal of Computational and Graphical Statistics 24, no. 1 (2015): 44–65. http://dx.doi.org/10.1080/10618600.2014.907095.

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49

Yamazawa, Akira, Yasuhiro Date, Keijiro Ito, and Jun Kikuchi. "Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach." Journal of Bioscience and Bioengineering 117, no. 3 (2014): 305–9. http://dx.doi.org/10.1016/j.jbiosc.2013.08.010.

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50

Yu, Zhonggen. "Visualizing Co-citations of Technology Acceptance Models in Education." Journal of Information Technology Research 13, no. 1 (2020): 77–95. http://dx.doi.org/10.4018/jitr.2020010106.

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With the rapid development of information and communication technologies, studies devoted to technology-assisted education have been soaring up. Nevertheless, the studies on technology acceptance model (TAM) appear relatively fewer. This study, through reviewing high quality papers, analyzed the co-citations of TAM on the basis of the basic TAM and numerous extended TAMs. Co-citations of TAM were revealed and discussed in terms of citation counts, bursts, betweenness centrality, and sigma. It also reviews the basic concept underlying user acceptance models, as well as its extended TAMs in deta

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