• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.383-392
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• 2000

Since late 1970, methods of influence or sensitivity analysis for detecting influential observations have been studied not only in regression and related methods but also in various multivariate methods. If results of multivariate analyses sometimes depend heavily on a small number of observations, we should be very careful to draw a conclusion. Similar phenomena may also occur in the case of incomplete data. In this research we try to study such influential observations in multivariate statistical analysis of incomplete data. Case of principal component analysis is studied with a numerical example.

• Korean Institute of Interior Design Journal
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• v.24 no.2
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• pp.142-150
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• 2015

The purpose of this study is to analysis the change of rural house type and house plans in Bonghwa province. According to definition of rural area, the scopes of the research of rural houses limited the Bonghwa rural area(1 eup, 9 myeon). The method of study is to compare and analyze about housing situation, structure of house, housing type and construction of house space etc. through the statistical data of Bongwha statistical yearbook, space syntax(convex analysis) and other various data etc. during these 10 years. As a results of the analysis 1) According to Change of family member the supply ratio of detached house is steadily decreasing and changing from a detached house to multi-household house in Bongwha areas. 2) Most of houses structure were using lightweight steel construction because of cost-cutting of construction and easy way to construct etc.. 3) The highest Integration space is living space in rural house plans 4) The highest segregation space is bathroom space of master bed room in rural house plans. Some of bed rooms are classed as segregation space regardless of Integration space 5) Traditional front yard's function is changing from the place with the various functions to the place with the specific functions.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.825-828
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• 2010

To classify Glycyrrhiza species, samples of different species were analyzed by $^1H$ NMR-based metabolomics technique. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the 1H NMR data sets. There was a clear separation between various Glycyrrhiza species in the PLS-DA derived score plots. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of various Glycyrrhiza species were lactic acid, alanine, arginine, proline, malic acid, asparagine, choline, glycine, glucose, sucrose, 4-hydroxy-phenylacetic acid, and formic acid. The compounds present at relatively high levels were glucose, and 4-hydroxyphenylacetic acid in G. glabra; lactic acid, alanine, and proline in G. inflata; and arginine, malic acid, and sucrose in G. uralensis. This is the first study to perform the global metabolomic profiling and differentiation of Glycyrrhiza species using $^1H$ NMR and multivariate statistical analysis.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.123-128
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• 2020

Radiation Following the 2011 Fukushima nuclear accident in Japan, public interest and anxiety about radiation safety increased, and vague anxiety about commonly exposed living radiation was generated. The Atomic Energy Safety Commission has been conducting a survey of processed products that advertise "negative ions" and "far-infrared" emissions under the Living Radiation Safety Management Act. In this study, in-depth analysis was performed from a statistical point of view using the measurement data presented in the Nuclear Safety Committee's actual survey analysis report as secondary data. As a result, there was a statistically significant difference (p<0.005) between latex and civil affairs products. There were also statistically significant differences (p<0.05) in the results of testing whether there were significant differences in the annual exposure dose between groups after categorizing 71 civil products, including radon beds, into bed, bedding, and living and other categories. The correlation analysis results also confirm that, as is commonly known, the annual doses received from processed products are associated with radon derived from U-238 and Th-232.

• Journal of Ocean Engineering and Technology
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• v.20 no.3 s.70
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• pp.54-60
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• 2006

For optimal design of a deep-sea ocean mining collector system, based on self-propelled mining vehicle, it is imperative to develop and validate the dynamic model of a tracked vehicle traveling on soft deep seabed. The purpose of this paper is to evaluate the fidelity of the dynamic simulation model by means of response surface methodology. Various statistical techniques related to response surface methodology, such as outlier analysis, detection of interaction effect, analysis of variance, inference of the significance of design variables, and global sensitivity analysis, are examined. To obtain a plausible response surface model, maximum entropy sampling is adopted. From statistical analysis and prediction for dynamic responses of the tracked vehicle, conclusions will be drawn about the accuracy of the dynamic model and the performance of the response surface model.

• Journal of the Korean Society of Safety
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• v.17 no.1
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• pp.94-98
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• 2002

This study aimed to search for the fundamental accident causes using a categorical analysis, a kind of statistical methods. As the analysis methods, correlation analysis, independence test and logistic regression analysis were used. And the SPSS package, a general-purpose mathematical library, was used to obtain statistical characteristics. As the result of this study, the accident causes associated with factor of 'lost working days' were factors such as 'employed periods', 'sex', 'type of accident', 'month'. In case of applying independence test method, the most important cause was the factor of 'month'. In case that logistic regression analysis method was applied, the cause contributed to the increase structure'. 'less than 6 month'. On the basis of these results, the plan for accident prevention and the proper investment for accident prevention expenditure could be carried out in each workshop.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.2
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• pp.184-189
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• 2000

This study was conducted to develop the objective evaluation method for tobacco smoke. The evaluation was carried out by using the data of cut or blended tobacco components, smoke components, electric nose system (ENS), and sensory test. By using the statistical methods, such as cluster analysis, discriminant analysis, factor analysis, correlation analysis, and multiple regression analysis, the relationship among the data of tobacco, smoke, ENS, and sensory evaluation was studied. By the results of cluster analysis, the data from smoke analysis by GC and ENS were able to select the difference of tobacco leaf characteristics. As the results of discriminant analysis, grouping by the components of tobacco leaves and smoke was possible and the results of GC analysis of smoke could be used for discrimination of tobacco leaves. In the results of factor analysis, nicotine, tar, CO, puff No and pH in the smoke were the factors effecting on the tobacco leaf characteristics. From the correlation analysis, aroma, taste, irritation, and smoke volume of sensory test had high relation to tar, p-cresol threonolatone, levoglucosane, and quinic acid- ${\gamma}$ -lactone of smoke. The ENS data showed high efficiency for discriminant analysis and cluster analysis, but it was not good for factor analysis, and correlation analysis. It was possible to estimate tobacco leaves and their blending characteristics by the analytical data of tobacco leaves, smoke, ENS, and sensory test results. By the multiple regression analysis, some correlation among selected chemical components and sensory evaluation were found. This study strongly indicated that the some chemical analysis data was available for the objective evaluation of tobacco sensory attributes.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.259-269
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• 2012

Purpose: The purpose of this study was to find out that the statistical method of stockpile reliability of ammunition by items can be applied to the reliability assessment of stockpile ammunition. Methods: We reviewed the statistical method of stockpile reliability of ammunition by items and verified the possibility of its application by case study. Results: We found that the statistical method of stockpile reliability of ammunition by items is very useful and effective to present the reliability of ammunition based on each item and to predict the change of the reliability in the future. The reliability of proximity fuse was about 94.5% and was influenced by manufacture's year and the difference between lot and lot more than storage period. Conclusion: The statistical method of stockpile reliability of ammunition by items can be applied to the reliability assessment of various stockpile ammunitions such as ammunition for mortar and canon.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.