Statistically Sound Visual Data Analysis - Revision history

Hua Guo: /* References */

2017-03-21T03:41:42Z

References

← Older revision		Revision as of 03:41, 21 March 2017
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	* Visualization of statistics parameters		* Visualization of statistics parameters
	** [https://publik.tuwien.ac.at/files/PubDat_242383.pdf Visual Encodings of Temporal Uncertainty: A Comparative User Study] Evaluation of six methods for visualization temporal uncertainty, three of which are for statistical uncertainty.		** [https://publik.tuwien.ac.at/files/PubDat_242383.pdf Visual Encodings of Temporal Uncertainty: A Comparative User Study] Evaluation of six methods for visualization temporal uncertainty, three of which are for statistical uncertainty.
			* Integrating statistics and visualizations for exploratory analysis
			** [http://hcil.cs.umd.edu/trs/2008-03/2008-03.pdf Integrating Statistics and Visualization: Case Studies of Gaining Clarity during Exploratory Data Analysis] "Statistics" refers to network metrics specifically, e.g. node degrees, betweenness, closeness. The paper focus on using statistics to help with navigation (e.g. filter and zoom based on network metric values).

Hua Guo: /* References */

2017-03-21T03:37:36Z

References

← Older revision		Revision as of 03:37, 21 March 2017
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	* Visual estimation of coefficients of statistical models		* Visual estimation of coefficients of statistical models
	** [http://journals.sagepub.com/doi/pdf/10.1057/ivs.2008.13 Judging correlation from scatterplots and parallel coordinate plots], Li, Martens, and van Wijk, 2008. This paper reports experimental results on how well people can assess correlation under varying sample size and visualization methods (scatter plots v.s. parallel coordinate plots). The results suggest that the accuracy of statistical judgements varies across visualization designs, motivating careful analysis and experimentation when designing visualizations to facilitate statistical data analysis.		** [http://journals.sagepub.com/doi/pdf/10.1057/ivs.2008.13 Judging correlation from scatterplots and parallel coordinate plots], Li, Martens, and van Wijk, 2008. This paper reports experimental results on how well people can assess correlation under varying sample size and visualization methods (scatter plots v.s. parallel coordinate plots). The results suggest that the accuracy of statistical judgements varies across visualization designs, motivating careful analysis and experimentation when designing visualizations to facilitate statistical data analysis.
			* Visualization of statistics parameters
			** [https://publik.tuwien.ac.at/files/PubDat_242383.pdf Visual Encodings of Temporal Uncertainty: A Comparative User Study] Evaluation of six methods for visualization temporal uncertainty, three of which are for statistical uncertainty.

Hua Guo: /* References */

2017-03-01T15:58:51Z

References

← Older revision		Revision as of 15:58, 1 March 2017
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	* [https://arxiv.org/pdf/1612.01040.pdf Controlling False Discoveries during Interactive Data Exploration]: the paper reports case study results on applying a new procedure for controlling FDR during data exploration and reports; not much emphasis on the interface yet but it sounds like Emanuel is working on that; the case study could provide inspirations for ways in which visualizations could help users better understand the FDR control procedural.		* [https://arxiv.org/pdf/1612.01040.pdf Controlling False Discoveries during Interactive Data Exploration]: the paper reports case study results on applying a new procedure for controlling FDR during data exploration and reports; not much emphasis on the interface yet but it sounds like Emanuel is working on that; the case study could provide inspirations for ways in which visualizations could help users better understand the FDR control procedural.
			* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427907/ Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests]: presents a graphical representation to illustrate test procedures that control family-wise error rate (FWER)

	Previous work on visualizations and statistical analysis		Previous work on visualizations and statistical analysis

David Laidlaw: /* Overview */

2017-02-28T17:06:15Z

Overview

← Older revision		Revision as of 17:06, 28 February 2017
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	== Overview ==		== Overview ==

	This page documents our research effort towards developing visual interfaces that help people perform statistically sound data analysis.		This page documents our research effort towards developing visual interfaces that help people perform statistically sound visual data analysis.

	== Proposals ==		== Proposals ==

Hua Guo: /* References */

2017-02-25T23:46:57Z

References

← Older revision		Revision as of 23:46, 25 February 2017
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	* [http://cidrdb.org/cidr2017/papers/p56-binnig-cidr17.pdf Towards Sustainable Insights (or why polygamy is bad for you)]: outlines a few examples of false discovery issues in existing visual data exploration (e.g. [http://www.vldb.org/pvldb/vol8/p2024-crotty.pdf Vizdom]) and visual recommendation tools (e.g. [http://www.vldb.org/pvldb/vol8/p2182-vartak.pdf SeeDB])		* [http://cidrdb.org/cidr2017/papers/p56-binnig-cidr17.pdf Towards Sustainable Insights (or why polygamy is bad for you)]: outlines a few examples of false discovery issues in existing visual data exploration (e.g. [http://www.vldb.org/pvldb/vol8/p2024-crotty.pdf Vizdom]) and visual recommendation tools (e.g. [http://www.vldb.org/pvldb/vol8/p2182-vartak.pdf SeeDB])

			Interactive control of false discover rate during data exploration

			* [https://arxiv.org/pdf/1612.01040.pdf Controlling False Discoveries during Interactive Data Exploration]: the paper reports case study results on applying a new procedure for controlling FDR during data exploration and reports; not much emphasis on the interface yet but it sounds like Emanuel is working on that; the case study could provide inspirations for ways in which visualizations could help users better understand the FDR control procedural.

	Previous work on visualizations and statistical analysis		Previous work on visualizations and statistical analysis

Hua Guo: /* Proposals */

2017-02-25T22:06:41Z

Proposals

← Older revision		Revision as of 22:06, 25 February 2017
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	* BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate		* BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate
	** Use cases		** [[Use cases]]

	== References ==		== References ==

Hua Guo: /* References */

2017-02-25T18:25:05Z

References

← Older revision		Revision as of 18:25, 25 February 2017
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	** [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability], Ottley et al., 2016		** [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability], Ottley et al., 2016
	** [http://www.aviz.fr/bayes Assessing the effect of visualizations on bayesian reasoning through crowdsourcing], Micallef, Dragicevic, and Fekete, 2012		** [http://www.aviz.fr/bayes Assessing the effect of visualizations on bayesian reasoning through crowdsourcing], Micallef, Dragicevic, and Fekete, 2012
			* Visual estimation of coefficients of statistical models
			** [http://journals.sagepub.com/doi/pdf/10.1057/ivs.2008.13 Judging correlation from scatterplots and parallel coordinate plots], Li, Martens, and van Wijk, 2008. This paper reports experimental results on how well people can assess correlation under varying sample size and visualization methods (scatter plots v.s. parallel coordinate plots). The results suggest that the accuracy of statistical judgements varies across visualization designs, motivating careful analysis and experimentation when designing visualizations to facilitate statistical data analysis.

Hua Guo: /* References */

2017-02-25T18:06:27Z

References

← Older revision		Revision as of 18:06, 25 February 2017
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	Previous work on visualizations and statistical analysis		Previous work on visualizations and statistical analysis

	* [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability ]		* Bayesian reasoning
			** [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability], Ottley et al., 2016
			** [http://www.aviz.fr/bayes Assessing the effect of visualizations on bayesian reasoning through crowdsourcing], Micallef, Dragicevic, and Fekete, 2012

Hua Guo: /* References */

2017-02-25T18:01:40Z

References

← Older revision		Revision as of 18:01, 25 February 2017
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	Vision and use case papers		Vision and use case papers

	*		* [http://cidrdb.org/cidr2017/papers/p56-binnig-cidr17.pdf Towards Sustainable Insights (or why polygamy is bad for you)]: outlines a few examples of false discovery issues in existing visual data exploration (e.g. [http://www.vldb.org/pvldb/vol8/p2024-crotty.pdf Vizdom]) and visual recommendation tools (e.g. [http://www.vldb.org/pvldb/vol8/p2182-vartak.pdf SeeDB])

	Previous work on visualizations and statistical analysis		Previous work on visualizations and statistical analysis

			* [http://pvis.github.io/introduction_to_vis/paper/Improving%20Bayesian%20Reasoning%20The%20Effects%20of%20Phrasing,%20Visualization,%20and%20Spatial%20Ability.pdf Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability ]

Hua Guo: Created page with "== Overview == This page documents our research effort towards developing visual interfaces that help people perform statistically sound data analysis. == Proposals == * BI..."

2017-02-25T17:44:51Z

Created page with "== Overview == This page documents our research effort towards developing visual interfaces that help people perform statistically sound data analysis. == Proposals == * BI..."

New page

== Overview ==

This page documents our research effort towards developing visual interfaces that help people perform statistically sound data analysis.

== Proposals ==

* BIGDATA: F: Statistically Sound and Computationally Efficient Massive Data Analysis - Sample Complexity, Uniform Convergence, and False Discovery Rate
** Use cases

== References ==

Vision and use case papers

*

Previous work on visualizations and statistical analysis