• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.435-449
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• 1998

In this paper, we consider the problem of constructing the lower cofidence intervals for the reliability P(X < Y z,w), where the stress X and the strength Y are the random variables with explanatory variables z and w, respectively. As an estimator of the reliability, a Mann-Whitney type statistic is considered. It is shown that under regularity conditions, the proposed estimator is asymptotically normal. Based on the result, the distribution free lower confidence intervals are constructed.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.358-366
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• 2011

The purpose of this study was to analyze the monthly and seasonal PM10 data using the Autoregressive Error (ARE) model at the southern part of the Gyeonggi-Do, Pyeongtaek monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables. The six meteorological variables are daily maximum temperature, wind speed, amount of cloud, relative humidity, rainfall, and global radiation. The four air pollution variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result shows that monthly ARE models explained about 17~49% of the PM10 concentration. However, the ARE model could be improved if we add the more explanatory variables in the model.

• Journal of the Korean Home Economics Association
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• v.38 no.1
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• pp.39-49
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• 2000

This research investigated the effect of socio-economic variables and economic distress variables on the university students' coping behavior in meal management. The data used in this research included 544 university students in Ulsan Areas. The independent explanatory power of socio-economic variables was larger than economic distress variables. But the explanatory power was increased in the regression analysis model that was included both the socio-economic variables and the economic distress variables. The influencing variables that effected the level of coping behavior in meal management were the amount of discretionary expenditure, gender, status of housing, employment distress and income distress.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.193-203
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• 1997

In this paper, we obtain some a, pp.oximate confidence intervals for the reliability of the stress-strength model when the stress and strength each depend on some explanatory variables, respectively. Also we compare the confidence intervals via Monte Carlo simulation.

• Journal of Fashion Business
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• v.6 no.2
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• pp.124-136
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• 2002

This objective study were to classify the contents of makeup behavior, to investigate the relationship between makeup behavior and sex role identity, and to examine how the makeup behavior, makeup satisfaction was influenced by sex role identity and demographics. To achieve this, the researchers surveyed 162 women for the ages of 18 through 25. The result of this study are followed. 1) Four factor of makeup behavior were sexual attractiveness, aesthetic, psychological dependence and makeup interest. 2) There were significant positive relationship between makeup behavior and sex role identity. 3) Sexual attractiveness were influenced by femininity, income. The explanatory power of the 2 variables were 8.5%. Aesthetic were influenced by masculinity. The explanatory power of the 1 variable was 9.2%. Psychological dependence were influenced by femininity. The explanatory power of the 1 variable was 8.2%. Makeup interest were influenced by masculinity, age. The explanatory power of the 2 variables were 9.0%. 4 Makeup satisfaction were influenced by sexual attractiveness, aesthetic. The explanatory power of the 2 variables were 22.1%.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.593-601
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• 2013

We often need to include categorial variables as explanatory variables in regression models. The categorial variables in regression models can be quantified through dummy variables. In this study, we provide an education tool using Excel VBA for displaying regression lines along with test results for regression models with a continuous explanatory variable and one or two categorical explanatory variables. The regression lines with test results are provided step by step for the model(s) with interaction(s), the model(s) without interaction(s) but with dummy variables, and the model without dummy variable(s). With this tool, we can easily understand the meaning of dummy variables and interaction effect through graphics and further decide which model is more suited to the data on hand.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.301-309
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• 2013

This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.123-129
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• 2016

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.