• The Mathematical Education
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• v.42 no.4
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• pp.561-577
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• 2003

Because statistical concepts are important parts in school mathematics, mathematics teachers have trained by special education model. In this study, a desirable direction of curriculum on probability and statistics at pre-service for mathematics teacher is considered. We proposed four subjects as Exploration and Analysis of Data for Mathematics Teacher, Probability and Statistics I, II for Mathematics Teacher and Statistical Software for Mathematics, and suggested the constituents and something being kept in mind for each subject.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• Journal of Astronomy and Space Sciences
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• v.32 no.1
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• pp.33-38
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• 2015

Forbush Decreases (FD) are transient, sudden reductions of cosmic ray (CR) intensity lasting a few days, to a week. Such events are observed globally using ground neutron monitors (NMs). Most studies of FD events indicate that an FD event is observed simultaneously at NM stations located all over the Earth. However, using statistical analysis, previous researchers verified that while FD events could occur simultaneously, in some cases, FD events could occur non-simultaneously. Previous studies confirmed the statistical reality of non-simultaneous FD events and the mechanism by which they occur, using data from high-latitude and middle-latitude NM stations. In this study, we used long-term data (1971-2006) from middle-latitude NM stations (Irkutsk, Climax, and Jungfraujoch) to enhance statistical reliability. According to the results from this analysis, the variation of cosmic ray intensity during the main phase, is larger (statistically significant) for simultaneous FD events, than for non-simultaneous ones. Moreover, the distribution of main-phase-onset time shows differences that are statistically significant. While the onset times for the simultaneous FDs are distributed evenly over 24-hour intervals (day and night), those of non-simultaneous FDs are mostly distributed over 12-hour intervals, in daytime. Thus, the existence of the two kinds of FD events, according to differences in their statistical properties, were verified based on data from middle-latitude NM stations.

• Korean Journal of Food Science and Technology
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• v.44 no.5
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• pp.503-525
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• 2012

Consumers are increasingly concerned about the origin of foods, so the geographical origin of foods has been a major topic of debate and extensive research. Various instrumental methods (e.g. high performance liquid chromatography (HPLC), gas chromatography (GC), capillary electrophoresis (CE), electronic nose, near-infrared spectroscopy (NIRS), nuclear magnetic resonance spectroscopy (NMR), DNA analysis, multi-isotope analysis) in conjunction with statistical analysis, were developed and applied in attempt to provide reliable answers to their geographical origin. This study reviews current developments in the application of various methods for a clear geographical origin of foods. The limitation of discrimination analysis for geographical origin was also discussed.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.337-346
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• 1999

Simple correspondence analysis is a technique for giving a joint display of points representing both the rows and columns of an n$\times$p two-way contigency table. In simple correspondence analysis, the singular value decomposition is the main algebraic tool. But, Choi and Huh(1996) pointed out the singular value decomposition is not robust. Instead, they developed a robust singular value decomposition and provided applications in principal component analysis and biplots. In this article, by using the analogous procedures of Choi and Huh(1996), we derive a robust version of simple correspondence analysis.

• IE interfaces
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• v.15 no.4
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• pp.409-425
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• 2002

This case study provides a market analysis methodology for overseas markets by applying statistical tools and the Six Sigma approach. The study suggests a procedure with seven steps to improve brands position in the market. These steps consist of interviewing consumers and floor salesmen of stores, surveying, analysis of correlation between brand position and customers satisfaction, analysis of relationship with companies and customer satisfaction factors, analysis of the customer satisfaction gap between companies, evaluating the importance of customer satisfaction factors, and suggestion for enhancement of brand position. The Six Sigma approach such as "Define", "Measure" and "Analyze" is used in this procedure, which is part of Six Sigma procedure, D-M-A-I-C (Define, Measure, Analyze, Improve, Control). Minitab and SAS are used for the statistical analysis.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.505-514
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• 2002

Multiple correspondence analysis graphically shows the correspondent relationship among categories in multi-way contingency tables. It is well known that the proportions of the principal inertias as part of the total inertia is low in multiple correspondence analysis. Moreover, although this problem can be overcome by using the Benzecri formula, it is not enough to show clear correspondent relationship among categories (Greenacre and Blasius, 1994, Chapter 10). In addition, they show that Andrews' plot is useful in providing the correspondent relationship among categories. However, this method also does not give some concise interpretation among categories when the number of categories is large. Therefore, in this study, we will easily interpret the multiple correspondence analysis by applying the K-means clustering analysis.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.335-346
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• 2015

The purpose of this study is to compare not only network influence of individual coauthor but also the types and properties of two coauthor networks of Communications for Statistical Applications and Methods and the Korean Journal of Applied Statistics which are published by the Korean Statistical Society using social network analysis.As the result of two network structure comparison, density, inclusiveness, reciprocity and clustering coefficient which represent the type of coauthor networks show almost similar values and the Korean Journal of Applied Statistics has bigger values in average degree, average distance and diameter because it has more nodes than Communications for Statistical Applications and Methods. Finally two journals have very similar type of coauthor network. In the comparison of network centrality of two coauthor networks, closeness centrality and betweenness centrality of the Korean Journal of Applied Statistics are bigger than those of Communications for Statistical Applications and Methods at the statistical significance level 0.05. The coauthor network of the Korean Journal of Applied Statistics has faster information delivery and stronger betweenness than that of Communications for Statistical Applications.

• Journal of Ginseng Research
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• v.43 no.4
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• pp.508-516
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• 2019

Background: Ginsenosides are not only the principal bioactive components but also the important indexes to the quality assessment of Panax ginseng Meyer. Their contents in cultivated ginseng vary with the growth environment and age. The present study aimed at evaluating the significant difference between 36 cultivated ginseng of different cultivation areas and ages based on the simultaneously determined contents of 14 ginsenosides. Methods: A high-performance liquid chromatography (HPLC) coupled with triple quadrupole mass spectrometer (MS) method was developed and used in the multiple reaction-monitoring (MRM) mode (HPLC-MRM/MS) for the quantitative analysis of ginsenosides. Multivariate statistical analysis, such as principal component analysis and partial least squares-discriminant analysis, was applied to discriminate ginseng samples of various cultivation areas and ages and to discover the differentially accumulated ginsenoside markers. Results: The developed HPLC-MRM/MS method was validated to be precise, accurate, stable, sensitive, and repeatable for the simultaneous determination of 14 ginsenosides. It was found that the 3- and 5-yr-old ginseng samples were differentiated distinctly by all means of multivariate statistical analysis, whereas the 4-yr-old samples exhibited similarity to either 3- or 5-yr-old samples in the contents of ginsenosides. Among the 14 detected ginsenosides, Rg1, Rb1, Rb2, Rc, 20(S)-Rf, 20(S)-Rh1, and Rb3 were identified as potential markers for the differentiation of cultivation ages. In addition, the 5-yr-old samples were able to be classified in cultivation area based on the contents of ginsenosides, whereas the 3- and 4-yr-old samples showed little differences in cultivation area. Conclusion: This study demonstrated that the HPLC-MRM/MS method combined with multivariate statistical analysis provides deep insight into the accumulation characteristics of ginsenosides and could be used to differentiate ginseng that are cultivated in different areas and ages.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.177-190
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• 1991

In this paper, we discuss and review various measures which have been presented for studying outliers. high-leverage points, and influential observations when principal component regression is adopted. We suggest several diagnostics measures when principal component regression is used. A numerical example is illustrated. Some individual data points may be flagged as outliers, high-leverage point, or influential points.