• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.45-60
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• 1987

Deciding whether a certain cancer patient is suffering from a bone marrow metastasis is quite essential to clinicians. To find a set of explanatory variables of the bone marrow metastasis, we employed the logistic regression analysis on 60 cancer patients with bone marrow metastasis (the case group) and 41 cancer patients without bone marrow metastasis (the control group). These data shown in Append were collected retrospectively from the record of Severance Hospital of Yonsei University College of Medicine from January, 1977 to December, 1985. We could establish a set of decision rules of the bone marrow metastasis specially designed for clinicians based on the explanatory variables of the best fitting logistic regression equation. We also compute the specifity and the sensistivity of our decision rules.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.229-244
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• 2007

Among the small area estimation methods, it has been known that hierarchical Bayesian(HB) approach is the most reasonable and effective method. However any model based approaches need good explanatory variables and finding them is the key role in the model based approach. As the lacking of explanatory variables, adopting the spatial terms in the model was introduced. Here in this paper, we evaluate the model based methods with/without spatial terms using the diagnostic methods which were introduced by Brown et al. (2001). And Economic Active Population Survey(2005) is used for data analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Journal of Korean Academy of Nursing
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• v.37 no.7
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• pp.1080-1090
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• 2007

Purpose: Although concerns of female sexual dysfunction (FSD) are increasing in Korea, sexual dysfunction related factors are limited in research studies. The aim of this study was to develop an explanatory model that will further explain the continuously increasing female sexual dysfunction cases in Korea. Methods: Survey visits were conducted to four hundred and eighty five women, over 25 years of age and presently residing in either urban or rural areas. All of them were analyzed using a structured questionnaire. A total of 8 instruments were used in this model. The analysis of data was done with both SPSS WIN for descriptive statistics and AMOS 5.0 for covariance structure analysis. Results: As a result, variables that showed notably direct effects on FSD were: sexual concept (sexual attitude), sexual distress, and psychosocial health (depression, crisis, traumatic life events). On the other hand, variables such as age, educational level, economic status, and marital status showed indirect influences on health-promoting behaviors. Conclusion: By comprehensively addressing the factors related to sexual dysfunction, and comparing each influence, this study can contribute to designing an appropriate sexual dysfunction prevention strategy in tune with the particular characteristics and problems of a client.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.366-370
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• 2013

Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper introduce Theil's method to find a fuzzy regression model which explain the relationship between explanatory variable and response variables. Theil's method is a robust method which is not sensive to outliers. Theil's method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. We propose an example to show Theil's estimator is robust than the Least squares estimator.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• Fourth Industrial Review
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• v.3 no.2
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• pp.1-10
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• 2023

Purpose - The main purpose of the paper is to examine the variables affecting carbon emissions in different nations around the world. Research design, data, and methodology - To measure its impact on carbon emissions, secondary data has data of the top 50 Countries have been taken. The stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model have been used to quantify the factors that affect carbon emissions. A modified version using Industry 4.0 and region in fundamental STIRPAT model has been applied with the ordinary least square approach. The outcome has been measured using both the basic and extended STIRPAT models. Result - Technology found a positive determinant as well as statistically significant at the alpha level of 0.001models indicating that technological innovation helps reduce carbon emissions. In total, 4 models have been derived to test the best fit and find the highest explaining capacity of variance. Model 3 is found best fit in explanatory power with the highest adjusted R2 (97.95%). Conclusion - It can be concluded that the selected explanatory variables population and Industry 4.0 are found important indicators and causal factors for carbon emission and found constant with all four models for total CO2 and Co2 per capita.

• The Journal of Fisheries Business Administration
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• v.48 no.2
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• pp.67-82
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• 2017

The study intends to examine the effects of the fishing license system on fisheries resources in order to reduce the adverse effects of recreational fishing, such as fishery resource reduction and environmental pollution. In doing so, the research question of the study is to determine what factors influence anglers' willingness to support fishing licenses. Based on the extended theory of planned behavior, we further included explanatory variables such as recreation specialization and motivations besides anglers' attitudes, norms and self-efficacy towards the environment and proposed six research hypotheses. The data were collected through on-site and online surveys in Gwangju and Cheonnam province and a total of 337 effective questionnaires were collected for data analysis. Three different binary logit models were employed with the dependent variable of anglers'willingness to support fishing licenses to assess the effects of explanatory variables. Study results show that social norms, the level of recreation specialization, motivation factors related to environmental experiences positively affected anglers'willingness to support fishing licenses. However, anglers'consumptive orientation attitudes such as catching big fish, motivation factors related to activity general experience preferences and previous fishing experience had negative effects on the dependent variables. Study results indicate that public outreach and education programs are essential to successfully introduce the fishing license system. Managerial and policy-related implications are further discussed to make recreational fishing a more environment-friendly recreational activity. This study investigated the effects of diverse variables derived from anglers' social-psychological characteristics on their support for fishing licenses and suggest diverse policy-related and managerial implications.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.