• Journal of Digital Convergence
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• v.15 no.5
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• pp.487-494
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• 2017

The purpose of this study was to identify factors which predict problem drinking on adults. Using the data on the Korea Welfare Panel Study for the 7th year, 3,915 people responded to the demographic factor, psychosocial factors and drinking behavior. And the logistic regression analysis was conducted to identify predictors of problem drinking. As a result, 36 percent of those surveyed showed that the problem drinking group. Gender, age, education, occupation, economic status, self-esteem, depression, and satisfaction of family and social relationships were correlated to alcohol use. In addition, the results of logistic regression, gender, age, education, job, self-esteem, depression were predicted problem drinking. Based on these findings, it is recommended practical counterplan that prevention of the problem drinking.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.285-288
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• 2003

신용위험 관리에서 필수적인 방법론이 스코어 카드이며 이를 작성하는 데에 있어서 널리 쓰이는 방법 중의 하나가 로지스틱 회귀분석이다. 본 논문에서는 로지스틱 회귀 방법에 기초한 스플라인 방법론을 소개하고자 한다. 최종 스코어 카드는 연속형 변수를 범주형 변수화 하므로 조각 선형 스플라인을 채택하였다. 모의 실험을 통하여 제안된 방법의 성 능을 규명 하였다.

• The Journal of Engineering Geology
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• v.14 no.2
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• pp.211-222
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• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• The KIPS Transactions:PartD
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• v.12D no.7 s.103
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• pp.1017-1022
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• 2005

There are many changes in the software requirements during the whole software life cycle. These changes require modification of the software, and it is important to keep software quality and stability while we are modifying the software. Refactoring is one of the technology to keep software quality and stability during the software modification; there are many researches related to automatic refactoring. In this paper, we propose three factors for Move Method which is one of the refactoring technique. We applied binomial logistic analysis to data which were extracted from sample program by each factor. The result of this process was very close to the result of manual analysis by program experts. Furthermore, we found that these factors have major roll to determine Position of a method, and these factors can be used as a basis of finding optimal position of a method.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.120-125
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• 2009

본 논문에서는 통계적 분류방법을 이용하여 문화재 자료의 분석을 수행하였다. 분류방법으로는 선형판별분석, 로지스틱회귀분석, 의사결정나무분석, 신경망분석, SVM분석을 사용하였다. 각각의 분류방법에 대한 개념 및 이론에 대해 간략히 소개하고, 실제자료 분석에서는 "지역별 문화재 통계분석 및 모형개발 연구 1차(2008)"에 사용된 자료 중 익산시 자료를 근거로 매장문화재에 대한 분류방법별 적합모형을 구축하였다. 구축된 모형과 모의실험의 결과를 통해 각각의 적합모형에 대한 비교를 수행하여 모형의 성능을 비교하였다. 분석에 사용된 도구로는 최근 가장 관심을 갖는 R-project를 사용하였다.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.550-558
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• 2015

This study investigates the degree of intention-behavior gap in the entertainment tourism. Using the sample of international visitors to South Korea, we identified the inclined actor (who are interested in the entertainment performance and actually went to the entertainment performance) and inclined abstainer (who are interested in the entertainment performance but did not go to the entertainment performance). The results of logistic regression analysis show that the sample was more accurately classified when attitude and knowledge on K-entertainment were included as explanatory variables. More findings and implications are provided.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.51-60
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• 2018

This study aims to understand crucial factors affecting user's Fintech payment service adoption. On the basis of innovation diffusion theory and prior Fintech literature, this study classifies the influence factors of users' adoption of Fintech payment service into two dimensions - service dimension containing complexity, perceived benefit, trust in service provider and user dimension containing personal innovativeness and security breach experience. The data analysis results using binary logistic regression shows the negative direct effects of perceived risk, complexity, security accident experience on user's service adoption are statistically significant. Personal innovativeness has a positive effect on user's Fintech payment service adoption. The moderation effect of security accident experience is also significant at p<0.05.

• 대한예방의학회:학술대회논문집
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• 2001.07a
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• pp.271-281
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• 2001