• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.399-408
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• 2015

This paper presented airquality data through data visualization in several ways and described its characteristics related to statistical methods for analysis. Software R was used for visualization tools. The airquality data was measured in New York city from May to September of year 1973. First, simple, exploratory data analysis was done in terms of both data visualization and analysis to find out univariate characteristics. Then through data transformation and multiple regression analysis, model for describing the airquality level was found. Also, after some data categorization, overall feature of the data was explored using box plot and three-dimensional perspective drawing and scatter plot.

• Journal of the Ergonomics Society of Korea
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• v.28 no.4
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• pp.117-124
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• 2009

To inspect the quality of data collected from a large-scale body measurement and investigation project, it is necessary to establish a proper data editing process. The three-dimensional body measurement may have measuring errors caused from measurer's proficiency or changes in the subject's posture. And it may also have errors caused in the process of algorithm expressing the information obtained from the three-dimensional scanner into numerical values, and in the course of data-processing dealing with numerous data for individuals. When those errors are found, the quality of the measured data is deteriorated, and they consequently reduce the quality of statistics which was conducted on the basis of it. Therefore this study intends to suggest a new way to improve the quality of the data collected from the three-dimensional body measurement by proposing a working procedure identifying data errors and correcting them from the whole data processing procedure-collecting, processing, and analyzing- of the 2004 Size Korea Three-dimensional Body Measurement Project. This study was carried out into three stages: Firstly, we detected erroneous data by examining of logical relations among variables under each edit rule. Secondly, we detected suspicious data through independent examination of individual variable value by sex and age. Finally, we examined scatter-plot matrix of many variables to consider the relationships among them. This simple graphical tool helps us to find out whether some suspicious data exist in the data set or not. As a result of this study, we detected some erroneous data included in the raw data. We figured out that the main errors are not because of the system errors that the three-dimensional body measurement system has but because of the subject's original three-dimensional shape data. Therefore by correcting some erroneous data, we have enhanced data quality.