• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.597-607
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• 2008

ATIS(Advanced Traffic Information System) provide valuable information as the travel time and traffic congestion, detour, traffic accident information to drivers, so it is being in the spotlight. But so far, the study on the consumer satisfaction with providing traffic information is incomplete. So, this study run a Canonical discriminant analysis and a Canonical correlation analysis by a QuantificationIItheory based on a Traffic Information Satisfaction image data through questionnaires, and found out the factors with influence on the consumer satisfaction. And this study definitely found out the correlation between consumer's recognition and traffic information satisfaction through understanding the change on the recognition about traffic information satisfaction by a QuantificationItheory. Finally, this study found out the change on the sensibility recognition of drivers by running the principal component anlysis, developed the traffic information satisfaction evaluation model considering the change on the recognition by using the structural equation model.

• Journal of Gifted/Talented Education
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• v.22 no.1
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• pp.61-85
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• 2012

The OSPG(Observation Scale for Parents of the Gifted in Korean Traditional and Western Music) is one of the tools for screening of the Gifted in Korean traditional and Western music. So the validity and reliability of the OSPG has an influence on securing validity of the whole identification process. The purpose of this study is to examine the validity and reliability of the OSPG-2 that input screening processing, and to suggest systematization of identification processing for the Gifted in Korean traditional and Western Music. For the achievement of this purpose, this study administered the OSPG-2 to 261 gifted students aged between 13 and 16 in the middle or high school for the arts. To validate the OSPG-2, I analyzed items of the OSPG-2, and conducted the exploratory and confirmative factor analysis. I also examined the internal consistency. As a result of the exploratory factor analysis, I collected 40 items of the OSPG-2 finally. Furthermore, I could identify psychological construct of the Gifted in Korean traditional and Western Music. The consequence of confirmative factor analysis, the model provided good fit to observed data. As a result of reliability analysis, the internal consistency of the internal characteristic verified Cronbach's ${\alpha}$=.939, and the internal consistency of the external environmental characteristic verified Cronbach's ${\alpha}$=.887. This result of validity study reflects the fact that the OSPG-2 is a valid scale and is able to input to gifted educational field instantly.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.125-133
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• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.187-196
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• 2015

The team's performances were analyzed by evaluating the scores gained by their offense and the scores allowed by their defense. To evaluate the team's attacking and defending abilities, we also considered the factors that contributed the team's gained points or the opposing team's gained points? In order to analyze the outcome of the games, three prediction models were used such as decision trees, logistic regression, and discriminant analysis. As a result, the factors associated with the defense showed a decisive influence in determining the game results. We analyzed the offense and defense by using the response variable. This showed that the major factors predicting the offense were non-stop pass and attack speed and the major factor predicting the defense were the distance between right and left players and the distance between front line attackers and rearmost defenders during the game.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4881-4894
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• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.2
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• pp.318-321
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• 2011

In this study, when a physician make a diagnosis of the Pattern Identifications(PIs) of stroke patients, the development methods of the PIs classification function is considered by diagnostic questionnaire of the PIs for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PIs subtypes diagnosed by two clinical experts with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PIs using the 44 items-Fire&heat(19), Qi-deficiency(11), Yin-deficiency(7), Dampness phlegm(7)- of them was significant statistically by univariate analysis in 61 questionnaires totally, we make some comparisons of the results of discriminant analysis model and generalized logit model. The overall diagnostic accuracy rate of the PIs subtypes for discriminant model(74.37%) was higher than 3% of generalized logit model(70.09%).

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.305-305
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• 2017

많은 국가에서 중소기업의 기술혁신을 지원하기 위해서 다양한 직 간접적 지원 프로그램을 기획하고 운영한다. 이 연구는 R&D 투자 효율성을 제고하기 위한 간접 지원의 대표적인 사례 중에서 지식서비스, 특히 그중에서도 R&D 기획, 기술가치평가, 정보 지원 사업에 주목했다. 이 연구에서는 지식서비스의 일환으로 공공과 민간에서 다양하게 제공되고 있는 3가지 지원 사업을 대상으로 R&D 중소기업 중에서 수요 집단의 특징을 프로파일링 했다. 이런 프로파일링을 통해 기업의 해당 지식서비스 지원 적합성을 진단할 수 있는 계량적인 방법을 제안하고 나아가 이를 통한 처방 방법을 논의한다. 구체적인 연구 방법으로는 데이터 마이닝을 통한 분석 방법으로 수요 집단을 프로파일링 했으며, 전통적인 통계 방법인 판별분석 방법을 활용해서 구체적인 지식서비스 적합성 진단 모형을 제시했다. 본 연구에서는 특정 지식서비스 수요에 대한 중소기업의 특징을 프로파일링하기 위해서 설문정보를 활용했으며, 모형의 적정성을 검증하기 위해서 새로운 설문을 활용하는 등 총 6,600 기업에 대한 대규모 설문을 활용했다는데도 차별적 의의가 있다. 본 연구의 결과는 대규모 데이터를 바탕으로 한 중소기업에 대한 지식서비스 또는 R&D 진단과 처방 서비스를 제시해 중소기업과 정부 R&D 투자 효율성을 개선시키는데 기여할 것으로 기대된다.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.67-82
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• 2023

This study clarified a complicated cause-and-effect relationship about the manpower utilization of container terminals. This paper conducted confirmatory factor analysis and structural equation modeling using 5 factors: technology development, safety and security change, external environment, terminal operation, and organizational culture that have been derived in the first study(2021). The results imply that technology development and external environment have a direct effect while terminal operation and safety and security change have indirect effect on a manpower utilization of container terminals. Based on these results, this paper may provide various policy implications for port operators, developers, and managers.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.