• Journal of Information Processing Systems
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• v.20 no.1
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• pp.76-84
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• 2024

This study constructed a VAR model with cotton futures and spot price data from April 30, 2009 to November 16, 2022, for empirical analysis utilizing the Granger causality test to analyze the dynamic relationship between cotton futures and spot market prices in China. The impulse response function and variance decomposition analysis showed that the cotton spot prices at flowering have a causal relationship with each other; in terms of mutual influence and impact, futures prices are higher than spot prices. Finally, it proposed countermeasures and suggestions from the perspective of establishing a standardized cotton spot market, improving the laws and regulations of the cotton futures market and trading system, and optimizing the structure of investment subjects.

• Journal of Korean Biological Nursing Science
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• v.26 no.3
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• pp.206-217
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• 2024

Purpose: The study aimed to determine the key factors influencing health-promoting behavior and the behavioral intentions of eHealth consumers based on the health promotion model and technology acceptance model. Methods: This research involved a longitudinal path analysis. The study was conducted with 360 eHealth consumers aged over 18 years, employed in the top five categories of the Korean standard classification of occupations, and living in the five largest cities in South Korea. The data were analyzed using SPSS 22.0 and AMOS 25.0. Results: Health-promoting behaviors were directly supported by prior health-related behavior and behavioral intention, and indirectly supported by perceived ease of use, perceived usefulness, perceived benefit, self-efficacy, and behavioral intention. These variables accounted for 36.3% of the variance in health-promoting behavior. Conclusion: The findings serve as a framework that can help health professionals and health information providers understand how to encourage consumers using eHealth to engage in health-promoting behaviors.

• The Research Journal of the Costume Culture
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• v.3 no.2
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• pp.393-408
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• 1995

The purpose of this study was to segment the female college apparel market based on fashion lifestyle and to develop a profile of each segment regard to fashion information sources, clothing benefits sought, and store selection criteria. The data were collected through questionnaire by random sample of 522 female college students. By cluster analysis of lifestyle factors, three groups were identified. (fashion leaders, fashion followers and fashion aversion), Three groups were then compared through multivariate analysis of variance on 11 fashion sources, 10 clothing benefits sought and 90 store selective criteria. Significant difference were found among the three groups on all these variables which indicate that fashion lifestyle can be a useful base for segmenting female apparel market and these groups are unique in terms of fashion information sources, clothing benefits sought and store selective criterias.

• Korean Journal of Child Studies
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• v.20 no.3
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• pp.51-75
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• 1999

This study examined how the attachment styles of married mothers influence their marital conflicts and caring behavior and the behavior problems of their children. Subjects were 60 mothers and their 60 elementary school children. Data were analyzed with correlation, multiple analysis of variance and path analysis. Mothers with unstable attachment styles had more children with behavior problems and they had more serious marital conflicts. Mother's with preoccupied attachment styles experienced more marital conflict than all other styles. Seriousness of marital conflict was related to negative caring behavior and negative caring behavior was related to behavior problems in children. There were direct paths between the attachment style of mothers and both externalized and internalized behavior problems of children in the clinical group.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.2
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• pp.188-199
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• 2003

This study intended to analyze the differences in (general & apparel) color preference and possession of apparel color preference(POA) by influential factors(fashion involvement, age, body-size and body- cathexis). We collected data from 303 females in the ages of 20's and 40's living in Seoul. The results were as follows; As a result of factor analysis, the fashion involvement was categorized into three aspects: coordinating fashion involvement, opinion-leading fashion involvement, and innovating fashion involve ment. There were significant differences among fashion involvement groups in the color preference and POA. Also the significant differences in color preferences and POA by ages and body-size were found. But in the analysis based on body-cathexis, no noticeable variance between different groups were found.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.267-274
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• 2015

In the semiconductor manufacturing post-FAB process, predicting a package test result accurately in the wafer testing phase is a key element to ensure the competitiveness of companies. The prediction of package test can reduce unnecessary inspection time and expense. However, an analysing method is not sufficient to analyze data collected at wafer testing phase. Therefore, many companies have been using a summary information such as a mean, weighted sum and variance, and the summarized data reduces a prediction accuracy. In the paper, we propose an analysis method for Wafer Map Image collected at wafer testing process and conduct an experiment using real data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.1
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• pp.103-109
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• 1990

The application of the Log-Pearson Type m distribution recommended by Water Resources Council, U. S. A. for flood frequency analysis requires the estimation of the regionalized skew coefficient. In this study, regionalized skew coefficients are estimated using a weighted regression model which relates at-site skews based on logarithms of observed annual flood peak series to both basin characteristics and precipitation data in the Han river and the Nakdong river basin. The model is developed with weighted least squares method in which the weights are determined by separating residual variance into that due to model error and due to sampling error. As the result of analysis, regionalized skews are estimated as - 0.732 and - 0.575 in the Han river and the Nakdong river basin, respectively.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.373-376
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• 2004

To investigate the vertical petrological and hydrological characteristics of fractured rock, geophysical and chemical logging were executed at 5 boreholes installed in the study area. The geophysical and hydrochemical logging data were analysed by using principal components analysis (PCA). Three main variables from PCA explained 86.4% of total variance of geophysical log data. The PCA results showed that PCl is closely related to groundwater properties and PC2 and PC3 are influenced by rock and fracture properties. Hydrochemical analysis indicated the presence of highly fractrued zone at the depth of 60m.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.