• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1035-1041
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• 2012

This paper examines the effects of fundamental variables on stock returns. Therefore, the major purpose of this study is to identify fundamental variables having a systematical effect on the stock return. The paper uses panel data analysis. We find that the results of regressions say that firm size, book-to-market ratio(B/M), earning-to-price ratio(E/P), cash flow-to-price ratio(C/P) can explain the differences in average returns across stocks.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.475-481
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• 2015

We use polynomial regression instead of linear regression if there is a nonlinear relation between a dependent variable and independent variables in a regression analysis. The performance of polynomial regression, however, may deteriorate because of the correlation caused by the power terms of independent variables. We present a polynomial regression model for the numerical inversion of PGF and show that polynomial regression results in the deterioration of the estimation of the coefficients. We apply principal components regression to the polynomial regression model and show that principal components regression dramatically improves the performance of the parameter estimation.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.149-169
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• 2015

This study tried to find out how to overcome multicollinearity problems in the structural equation model by creating a hierarchical construct model about the repurchase intention of social commerce. This study selected, as independent variables, price, quality, service, and social influence, based on literature review about social commerce, and then, as detailed variables of independent variables, selected system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norms, and reputation. As results of empirical analysis about hierarchical construct model, all the independent variables were accepted having a significant impact on repurchase intention of social commerce. Next, this study analyzed the competition model that eight independent variables of price, system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norm, and reputation directly influence the repurchase intention of social commerce. As results of empirical analysis, system quality, information quality, transaction safety, communication appeared to be insignificant. This study showed that hierarchical construct model is useful to overcome the multicollinearity problem in structural equational model and to increase explanatory power.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.59-69
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• 1998

A regression model with basin physiographic characteristics as independent variables was calibrated for regional flood frequency analysis. In case that high correlations existing among the independent variables the ridge regression has been known to have capability of overcoming the problems of multicollinearity. To optimize the ridge regression model the cost function including regularization parameter must be minimized. In this research the genetic algorithm was applied on this optimization problem. The genetic algorithm is a stochastic search method that mimic the metaphor of natural biological heredity. Using this method the regression model could have optimized and stable weights of variables.

• Journal of Integrative Natural Science
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• v.14 no.1
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• pp.27-34
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• 2021

전 세계적으로 인공지능(AI)을 구현하려는 움직임이 많아지고 있다. AI구현에서는 많은 양의 데이터, 목적에 맞는 데이터의 분류 등 데이터의 중요성을 뺄 수 없다. 이러한 데이터를 생성하고 가공하는 기술에는 사물인터넷(IOT)과 빅데이터(Big-data) 분석이 있으며 4차 산업을 이끌어 가는 원동력이라 할 수 있다. 또한 이러한 기술은 국가와 개인 차원에서 많이 활용되고 있으며, 특히나 특정분야에 집결되는 데이터를 기준으로 빅데이터 분석에 활용함으로써 새로운 모델을 발견하고, 그 모델로 새로운 값을 추론하고 예측함으로써 미래비전을 제시하려는 시도가 많아지고 있는 추세이다. 데이터 분석을 통한 결론은 데이터가 가지고 있는 정보의 정확성에 따라 많은 변화를 가져올 수 있으며, 그 변화에 따라 잘못된 결과를 발생시킬 수도 있다. 이렇듯 데이터의 분석은 데이터가 가지는 정보 또는 분석 목적에 맞는 데이터 분류가 매우 중요하다는 것을 알 수 있다. 또한 빅데이터 분석결과 통계량의 신뢰성과 정교함을 얻기 위해서는 각 변수의 의미와 변수들 간의 상관관계, 다중공선성 등을 고려하여 분석해야 한다. 즉, 빅데이터 분석에 앞서 분석목적에 맞도록 데이터의 분류가 잘 이루어지도록 해야 한다. 이에 본 고찰에서는 AI기술을 구현하는 머신러닝(machine learning, ML) 기법에 속하는 분류분석(classification analysis, CA) 중 의사결정트리(decision tree, DT)기법, 랜덤포레스트(random forest, RF)기법, 선형분류분석(linear discriminant analysis, LDA), 이차선형분류분석(quadratic discriminant analysis, QDA)을 이용하여 데이터를 분류한 후 데이터의 분류정도를 평가함으로써 데이터의 분류 분석률 향상을 위한 방안을 모색하려 한다.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.123-136
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• 2017

The evaluation and analysis method of information system (IS) is studied from the system perspective, the user perspective, and the management viewpoint. The detailed analysis method performs qualitative evaluation by user questionnaire or expert opinion. In this study, Measures the productivity and the effect of building administrative information systems. In the previous study, qualitative productivity and universal effect indicators were used, but in this study, quantitative productivity indicators and indicators specific to administrative complaints were selected. KONEPS, an administrative service system, used electronic contract records and information recorded in the intermediate process. The information was converted into the number of days, and the productivity based on the input manpower was calculated. The effect analysis analyzed the questionnaire related to civil affairs, which is the goal of the administrative work system. Each factor was divided into reflective structural variable and formal structural variable, and internal consistency and multi-collinearity were diagnosed. In order to verify the model, the influence of the work was set as a hypothesis, the reliability was verified according to the descriptive statistics method, the influence was measured through the regression analysis, and the model was analyzed by the multiple regression model path coefficient. Model validation methods are Chi-square (df, p), RMR, GFI, AGFI, NFI, CFI and GFI as indicators according to CFA.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.642-651
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• 2018

This study aimed to verify the effects of parental chid-rearing attitudes on adolescents' aggressiveness and the mediation effects of peer attachment. To this end, the 7th year data (2016) (4th grade to the first year of high school) of Korea Children & Youth Panel Survey (KCYPS) were used. The results of this study are as follows: First, the correlations between child-rearing attitudes, aggressiveness, and peer attachment variables were confirmed. Second, parental child-rearing attitudes significantly affected adolescents' aggressiveness. Third, peer attachment showed some mediation effects in terms of the effects of parental child-rearing attitudes on adolescents' aggressiveness. For the significance verification of the mediation effects, the Sobel test was carried out. The results of this study are meaningful as empirical foundation. Based on the study results, this study suggested the need of education programs for parents and peer attachment consolidation programs for adolescents in relation with social welfare mediation practice.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.