• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.41-50
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• 2007

This study attempts to test the lead-lag relationship between the stock price and the business indicator in the multivariate context. It additionally investigates the short and long-run dynamic relationships among the four market variables. The hypothesis that the stock price leads the business indicator is found to be rejected for the whole study period. When structural change is considered, the statistical result appears to reflect the reality. The causal relationships among the variables in the former period are simpler than those in the latter period, and the stock price significantly appears to lead the business indicator. On the other hand, the relationship between the stock price and the business indicator in the latter period appears to prove the recent hypothesis of their coincidence.

• Journal of the Korean Solar Energy Society
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• v.35 no.5
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• pp.31-38
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• 2015

It is important to improve the energy performance in the industrial sector, and therefore most of the manufacturing companies need the energy performance indicators to identify the target and to verify the energy savings. However, the conventional energy performance indicators such as the total energy consumption and the energy intensity are not proper to use. The reason is that they do not consider adequate relevant variables including productions in the boundary of the manufacturing companies. Therefore, the study provides the advanced energy performance indicator using by the linear regression model according to each energy source to manage the target and to verify the energy performance properly.

• Journal of Korea Technology Innovation Society
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• v.9 no.1
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• pp.26-51
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• 2006

Recently technology commercialization has been the focus of technology Promotion Policy in Korea. This paper tries to develop a composite indicator for measuring trends and prospects of national technology commercialization through integrating large amount of information into easily understood formats. The indicator is composed of five sector indicators including activity, cooperation or networking, performance, environment, and psychology indicators, each of which aggregates five individual variables. The statistical method of standardizing and weighting variables in the aggregation process are also mentioned. This indicator is expected to be a useful monitoring and policy tool for the Korean technology market.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.1093-1117
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• 2016

Global sensitivity analysis (GSA) has been widely used to investigate the sensitivity of the model output with respect to its input parameters. In this paper a new single-solution search optimization algorithm is developed based on the GSA, and applied to the size optimization of truss structures. In this method the search space of the optimization is determined using the sensitivity indicator of variables. Unlike the common meta-heuristic algorithms, where all the variables are simultaneously changed in the optimization process, in this approach the sensitive variables of solution are iteratively changed more rapidly than the less sensitive ones in the search space. Comparisons of the present results with those of some previous population-based meta-heuristic algorithms demonstrate its capability, especially for decreasing the number of fitness functions evaluations, in solving the presented benchmark problems.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.84-96
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• 2022

Most of the islands of Korea are distributed in the South and West Sea, and it consists of independent small stream. As a result, the fish community that inhabits the island's stream is isolated from the mainland and other island. This study utilized a Self-Organizing Map (SOM) and a random forest model to analyze the relationship between environmental variables and fish communities inhabiting islands in South Korea. Through the SOM analysis, the fish communities were divided into three clusters, and there were differences in biotic and abiotic factors between these groups. Cluster I consisted of sites with relatively larger island areas and a higher number of species and population. It was found that 15 out of 16 indicator species were included. Meanwhile, the remaining clusters had fewer species and populations. Cluster II, especially, showed the lowest impact from physical variables such as water width and depth. As a result of predicting the species richness using the random forest model, physical variables in habitats, such as stream width and water depth, had a relatively higher importance on species richness. On the other hand, forest area was the most important variables for predicting Shannon diversity, followed by maximum water depth, and gravel. The results suggest that this study can be used as basic data for establishing a stream ecosystem management strategy in terms of conservation and protection of biological resources in streams of islands.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.227-237
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• 2014

We use the regression model with the indicator variables in the case that we use qualitative variables as some predictor variables in regression analysis. We use the ANCOVA(Analysis of Covariance) model when comparing the response variable among groups while statistically controlling for variation in the response variable caused by a variation in the covariate. DD-plot can be used as a graphical exploratory data analysis tool before the confirmatory data analysis. With the DD-plot, we can discriminate the difference of groups in the regression model with the indicator variables or the ANCOVA model at a glance. Making DD-plot does not demand the statistical model assumption about error terms in regression model. Several examples show the usefulness of DD-plots as a graphical exploratory data analysis tool for the regression analysis.

• Journal of Korean Academy of Nursing
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• v.21 no.2
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• pp.195-203
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• 1991

Correlations among body weight and sociodemographic factors, including life - style were tested as social determinants of health in a sample of 5,201 adults in Korea. The aim of this study was to determine the extent to which sociodemographic variables and life-style associated health behaviors explain body weight distribution. A second aim was to explain the relation of body weight and health status to stress the importance of body weight as an early risk indicator of health status. The canonical correlation between the weight distribution(underweight and overweight) and the independent variables was 0.29, 17% of the total variance was explained. Perceived health level represented the highest contribution(canonical coefficient 0.82) to body weight. Sociodemographic factors such as sex, economic status, and life-style factors such as smoking, exercise, regular meais and sleep showed comparatively high contributions to body weight. The relevance of body weight for health status including the rate of chronic disease and the rate of medical utilization was significant. Especially, underweight was clarified as being mere important than overweight to morbidity level and medical utilization. These findings suggest that perceived body weight is an important indicator of health status and is thus a valuable variable to be considered for nursing intervention and health education related to the promotion of health.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.11
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• pp.1605-1614
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• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2189-2196
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• 2006

The present work is concerned with the development of a procedure for adaptive computations of shear localization problems. The maximum jump of equivalent strain rates across element boundaries is proposed as a simple error indicator based on interpolation errors, and successfully implemented in the adaptive mesh refinement scheme. The time step is controlled by using a parameter related to the Lipschitz constant, and state variables in target elements for refinements are transferred by $L_2$-projection. Consistent tangent moduli with a proper updating scheme for state variables are used to improve the numerical stability in the formation of shear bands. It is observed that the present adaptive mesh refinement procedure shows an excellent performance in the simulation of shear localization problems.

• Environmental Analysis Health and Toxicology
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• v.30 no.sup
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• pp.3.1-3.11
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• 2015

Objectives This study presents the indicators and proxy variables for the quantitative assessment of green chemistry technologies and evaluates the relative importance of each assessment element by consulting experts from the fields of ecology, chemistry, safety, and public health. Methods The results collected were subjected to an analytic hierarchy process to obtain the weights of the indicators and the proxy variables. Results These weights may prove useful in avoiding having to resort to qualitative means in absence of weights between indicators when integrating the results of quantitative assessment by indicator. Conclusions This study points to the limitations of current quantitative assessment techniques for green chemistry technologies and seeks to present the future direction for quantitative assessment of green chemistry technologies.