• Journal of the Korean Statistical Society
• /
• 제27권4호
• /
• pp.435-449
• /
• 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.

• 한국대기환경학회지
• /
• 제27권3호
• /
• pp.358-366
• /
• 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.

• 대한가정학회지
• /
• 제38권1호
• /
• pp.39-49
• /
• 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
• /
• 제25권6호
• /
• pp.1293-1300
• /
• 2014

기온의 변화는 국가 정책에 여러 가지 영향을 준다. 본 연구에서는 충청남도 서산시 2003년 ~ 2012년 기온을 주위에서 쉽게 구할 수 있는 기상자료, 온실가스자료, 대기자료를 이용하여 자기회귀오차 (autoregressive error)모형으로 월별과 계절별로 분석하였다. 기온을 위한 기상자료로는, 풍속, 강수량, 일사량, 운량, 습도를 사용했고, 온실가스자료는 이산화탄소 ($CO_2$), 메탄 ($CH_4$), 아산화질소 ($N_2O$), 염화불화탄소 ($CFC_{11}$), 대기자료는 미세먼지 ($PM_{10}$), 이산화황 ($SO_2$), 이산화질소 ($NO_2$), 오존 ($O_3$), 일산화탄소 (CO)를 사용하였다. 분석 결과, 자기회귀오차모형으로 월별 기온을 39%-63% 정도 설명할 수 있다.

• 품질경영학회지
• /
• 제25권1호
• /
• pp.193-203
• /
• 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.

• 패션비즈니스
• /
• 제6권2호
• /
• pp.124-136
• /
• 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
• /
• 제24권3호
• /
• pp.593-601
• /
• 2013

회귀모형에서 범주형 변수를 독립변수로 포함시켜야 할 경우가 발생한다. 회귀모형의 범주형 변수는 가변수를 통해 수량화된다. 이 연구에서는 하나의 양적 독립변수와 하나 혹은 두 개의 범주형 독립변수를 가지는 회귀모형에 대해 가설검정 결과와 함께 회귀직선을 보여주는 교육용 도구를 엑셀 VBA (Visual Basic for application)를 통해서 구현한다. 가설검정 결과와 회귀직선은 교호작용이 포함된 모형, 교호작용이 없는 모형 및 가변수가 없는 모형에 대해 단계별로 제공된다. 이 교육도구를 통해 가변수와 교호작용의 의미를 더 쉽게 이해할 수 있으며, 나아가 어떤 모형이 주어진 자료에 가장 적합한지 그림을 통해 판단할 수 있게 된다.

• Communications for Statistical Applications and Methods
• /
• 제20권4호
• /
• pp.301-309
• /
• 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.

• 산업경영시스템학회지
• /
• 제39권1호
• /
• pp.123-129
• /
• 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.

• 산업경영시스템학회지
• /
• 제40권2호
• /
• pp.129-136
• /
• 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.