| dc.contributor.author | May, Thorsten | en_US |
| dc.contributor.author | Davey, James | en_US |
| dc.contributor.author | Kohlhammer, Jörn | en_US |
| dc.contributor.editor | Joern Kohlhammer and Daniel Keim | en_US |
| dc.date.accessioned | 2014-01-27T15:28:31Z | |
| dc.date.available | 2014-01-27T15:28:31Z | |
| dc.date.issued | 2010 | en_US |
| dc.identifier.isbn | 978-3-905673-74-6 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/PE/EuroVAST/EuroVAST10/045-050 | en_US |
| dc.description.abstract | In this work, we combine KVMaps, a visualization technique presented in [May07] for the visualization of statistical aggregations in multivariate contingency tables, with the measures used for the statistical Chi-Square goodness-of-fit test. Goodness-of-fit tests are used to check whether a given distribution of values matches an expected distribution. A single test statistic is calculated to represent the deviation of the complete dataset. By visualizing the deviations for all entries in the contingency table, it is possible to identify the patterns in the distribution of data items, which contribute most to the overall deviation of the dataset. We present two use cases to illustrate how the information about the patterns can be used. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation-Visualization | en_US |
| dc.title | Combining Details of the Chi-Square Goodness-of-Fit Test with Multivariate Data Visualization | en_US |
| dc.description.seriesinformation | EuroVAST 2010: International Symposium on Visual Analytics Science and Technology | en_US |
