• The Korean Journal of Emergency Medical Services
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• v.12 no.3
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• pp.129-135
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• 2008

Purpose : This study was to analyze the demand for emergency ambulance service and to characterize the factors associated with the demand. Method : The basis for the model was from the actual demand for public emergency ambulance and socioeconomic and geographic characteristics. Multiple regression analyses were done for the related characteristics of public ambulance service. Result : The model explained total demand with a high degree of accuracy : the coefficient of determination($R^2=0.96$). For the regression, the set of variables indicative of low socioeconomic status were all significant. It showed the inappropriate use of public ambulance system. Public ambulance demand increased in higher housing density, low income, male unemployment and female labor force. Conclusion : The demand for public ambulances appeared to be highly predictable, using a simple linear model employing socioeconomic variables, quality of service variables, and land use variables. Low-income families tended, to use the public ambulance system more often than higher income. Area having elderly people or children also made many calls. Estimated demand calls were stable and had a tendency to be similar incident types.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.58-65
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• 2011

Construction Productivity is one of the most important elements in construction management. It is used in construction process scheduling and cost management, which are significant sector in construction management. It is important to make appropriate schedule and monitor how works are done within schedule. But construction project contains uncertainty and inexactitude, modifying construction schedule is being an issue to manage construction works well. Even though prediction and monitoring of productivity can be principal activity, it is hard to predict productivity with manager's experience and a standard of estimate. A large number of factors influencing productivity, such as drawing, construction method, weather, labor, material, equipment, etc. But current calculation of productivity depends on empirical probability, not consider difference of each influencing factor. In this research, the aim is to present a productivity predicting regression model of form work, which includes effectiveness of influences factors. 5 variables existed inside form work are selected by interview and site research based on literature review of existed various productivity influencing factors. The effectiveness and correlation of productivity influencing factors are analyzed by statistical approach, and it is used to make productivity regression model. The finding of this research will improves monitoring and controlling of project schedule in construction phase.

• The Journal of Information Systems
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• v.6 no.2
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• pp.29-50
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• 1997

This paper addresses the issues of affecting factors to measure the satisfaction degree in PC communication utilizations. In order to develope an optimal model, we study appropriate affecting factors in PC communication utilizations through the focus group interviews with student users, and surveys the satisfaction levels that users have felt in services. Based on the optimal regression model, we suggest an appropriate satisfaction model in PC communication utilizations. That model shows that most users are interested in the A/S area for use. A/S factor is the most powerful one to the satisfaction model. Second one is usefulness, next is DB quality. But service-ability factor and convenience one are negative ones. Most users think that their factors are in the way of fluent communication. So to keep the competitiveness in the PC communication utilizations, the negative factors should be amended as soon as possible.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.513-521
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• 2020

In this paper, we have developed a Fama - French five factor model (FF5 model) from Fama & French (2015) by using concept of time-varying coefficient. For a data set, we have used monthly data form Kenneth R. French home page, it include Japan portfolios (classified by using size and book-to-market) and 5 factors from July 1990 to April 2020. The first analysis, we used Augmented Dickey-Fuller test (ADF test) for the stationary test, from the result, all Japan portfolios and 5 factors are stationary. Next analysis, we estimated a coefficient of Fama - French five factor model by using a generalized additive model with a thin-plate spline to create the time-varying coefficient Fama - French five factor model (TV-FF5 model). The benefit of this study is TV-FF5 model which can capture a different effect at different times of 5 factors but the traditional FF5 model can't do it. From the result, we can show a time-varying coefficient in all factors and in all portfolios, for time-varying coefficients of Rm-Rf, SMB, and HML are significant for all Japan portfolios, time-varying coefficients of RMW are positively significant for SM, and SH portfolio and time-varying coefficients of CMA are significant for SM, SH, and BM portfolio.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.1
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• pp.13-27
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• 2012

According to the development of Intelligent Transportation System technology, VMS(Variable Message Signs) are operating on highway. But, VMS display information which don't reflect traffic condition and driver's human factor. So driver can't read VMS message during limited time, it makes to reduce VMS's reliability. This paper presents a model for VMS message reading time and distance considering traffic condition and human factor. We built driving simulator by Winroad package which is able to copy real driving condition. Subjects were comprised of 20 people who reflect domestic driver's condition such as sex. We did regression analysis with experiment results and draw the model. The model could be possible to develop message- set considering traffic condition and human factor.

• Journal of Climate Change Research
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• v.7 no.2
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• pp.193-203
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• 2016

The importance of forecasting agricultural production is receiving attention while climate change is accelerating. This study suggested three types of crop yield forecasting model for major vegetable crops by using downscaled meteorological information of main production area on farmland level, which identified as limitation from previous studies. First, this study conducted correlation analysis with seven types of farm level downscaled meteorological informations and reported crop yield of main production area. After, we selected three types of meteorological factors which showed the highest relation with each crop species and regions. Parameters were deducted from meterological factor with high correlation but crop species number was neglected. After, crop yield of each crops was estimated by using the three suggested types of models. Chinese cabbage showed high accuracy in overall, while the accuracy of daikon and onion was quiet revised by neglecting the outlier. Chili and garlic showed differences by region, but Kyungbuk chili and Chungnam, Kyungsang garlic appeared significant accuracy. We also selected key meteorological factor of each crops which has the highest relation with crop yield. If the factor had significant relation with the quantity, it explains better about the variations of key meteorological factor. This study will contribute to establishing the methodology of future studies by estimating the crop yield of different species by using farmland meterological information and relatively simplify multiple linear regression models.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.211-220
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• 2011

TOD(Transit Oriented Development) has recently been active, which presents that TOD planning elements should be comprehensively taken into consideration in order to enhance domestic transit ridership by changing environments in rail station areas and an empirical analysis on the type of rail station areas and transportation demand should be a prerequisite for usage of future development planning. This study aims to grasp a variety of TOD of influence factors in Seoul rail station area and to perform analysis to identify relationship between public transportation demand and these TOD design factors. To make it come true, we gathered data with respect to Density, Diversity, and Accessibility as representative TOD planning elements and carried out factorial and regression analysis. Consequently, we drew 7 influence factors base on factorial analysis: Factor 1(Diversity/ -Use Mix(LUM)), Factor 2(Density/development density), Factor 3(Accessibility/public transportation facility supply), Factor 4(Design/street design), Factor 5(Green/access mode (pedestrian, bike), Factor 6(Design/subway size), Factor 7(Accessibility/Public transit operation) As the result of model development by using factorial and regression analysis, positive influence factors on passenger flow in rail station area are Factor 1(Diversity : Land-Use Mix), Factor 3(Accessibility : public transportation facility supply), Factor 2(Density : development density), Factor 5(Design/ access mode) and Factor 6(subway size) Next, negative influence factor on passenger flow in rail station area shows Factor 7(Accessibility/Public transit operation) as the most influential factor. This is because the growth of service interval of linked subway and bus leads to reduced demand.

• Korean Journal of Health Education and Promotion
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• v.33 no.2
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• pp.77-87
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• 2016

Objectives: This study was conducted to provide data in order to develop effective family planning programs by analyzing the factors which affect contraceptive use among women in Ethiopia. Methods: The community health survey was conducted on women of childbearing age. The research model was developed based on Andersen's Model. A total of 320 respondents were analyzed through Chi-square analysis, t-tests and logistic regression analysis. Results: Contraceptive prevalence rate was 43.1% and most of them were using modern and short-term methods. Among three factor categories, the need factor(unwanted pregnancy experience) was revealed as the most important factor, following enabling factor which was knowing the FP services in health center. Conclusions: These results lead to several conclusions. First, the results imply that programs should focus on women who are in the over 30yr. as well as the poorer economic group and urban residents. Second, to improve the effectiveness of the program, it is important to help them to be motivated themselves and to promote knowledge on various methods. Third, in terms of service delivery, community health workers are expected to take a crucial role. To improve the availability of services, they should provide practical services as those in health center.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.155-162
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• 2008

This study shows how to estimate the design hour factor when the counting stations don't have all of the hourly volumes such as in a coverage survey. A coverage survey records traffic volume from 1 to 5 times in a year so it lacks the detailed information to calculate the design hour factor. This study used the traffic volumes of permanent surveys to estimate the design hour factor in coverage surveys using correlation and regression analysis. A total 7 independent variables are used : the coefficient of variance of hourly volume, standard deviation of hourly volume, peak hour volume, AADT, heavy traffic volume proprotion, day time traffic volume proportion and D factor. All of variables are plotted on a curve, so it must use non-linear regression to analyze the data. As a result the coefficient of determination and MAE are good at logarith model using AADT.