• Journal of Digital Convergence
• /
• v.13 no.6
• /
• pp.125-132
• /
• 2015

It is current situation that a number of issues are being raised how the weight is calculated from customer satisfaction survey. This study investigated how the weight of satisfaction for each quantile is different by comparing ordinary least square regression model to quantile regression model and carried out bootstrap verification to find the influence difference of regression coefficient for each quantile. As the analysis result of using R(Quantreg package) that is open software, it appeared that there was the influence size of satisfaction factor along study result and quantile and there was the significant difference statistically regarding regression coefficient for each quantile. So, to use quantile regression model that offers the influence of satisfaction factor for each customer group along satisfaction level would contribute to plan the quantitative convergence policy for customer satisfaction.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.2 no.1
• /
• pp.35-40
• /
• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

• Health Policy and Management
• /
• v.26 no.1
• /
• pp.30-38
• /
• 2016

Background: This study purposed to analyze the relationship between spatial distribution of Diabetes prevalence rates and regional variables. Methods: The unit of analysis was administrative districts of city gun gu. Dependent variable was the age- and sex- adjusted diabetes prevalence rates and regional variables were selected to represent three aspects: demographic and socioeconomic factor, health and medical factor, and physical environment factor. Along with the traditional ordinary least square (OLS) regression analysis, geographically weighted regression (GWR) was applied for the spatial analysis. Results: Analysis results showed that age- and sex-adjusted diabetes prevalence rates were varied depending on regions. OLS regression showed that diabetes prevalence rates had significant relationships with percent of population over age 65 and financial independence rate. In GWR, the effects of regional variables were not consistent. These results provide information to health policy makers. Conclusion: Regional characteristics should be considered in allocating health resources and developing health related programs for the regional disease management.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2009.03a
• /
• pp.1043-1052
• /
• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

• Journal of Navigation and Port Research
• /
• v.27 no.2
• /
• pp.163-170
• /
• 2003

This empirical study takes the activation approach for the competitive power of Korean shipping companies in the Korea-China liner routes. Data for this study were collected from Korea/ China/ 3rd flag shipping companies through the 500 questionnaires. The data of 250 respondents were analyzed statistically to verify the hypotheses and to induce Regression Equation which could predicts the influencing level of the determinants to competitive advantage for Korean shipping companies on Korea-China Liner Shipping Routes. Factor Analysis/ Cronbach's Alpha/ Principal Analysis/ Multiple Regression Analysis were used in order to test the hypotheses for the empirical study.

• Journal of the Korean Statistical Society
• /
• v.26 no.4
• /
• pp.493-505
• /
• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2011.05a
• /
• pp.191-192
• /
• 2011

The construction projects contain a lot of variables and risk affecting productivity. The duration of the project must be recognized important as for quality, unit cost and safety. There is need for improving work efficiency by investigating relationship of works to prevent delay. This study focuses on analysing the delay factors of steel staircase system to suggest regression model that enables construction productivity estimation. The position of the observers and construction delay factors were expressed by the independent variable of the regression model and productivity was expressed by a dependent variable. This paper suggests quantitative productivity and it is expected that will be helpful estimating application in construction new technologies.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.3
• /
• pp.579-585
• /
• 2012

Many different semi-supervised learning algorithms have been proposed for use wit unlabeled data. However, most of them focus on classification problems. In this paper we propose a semi-supervised regression algorithm called the semi-supervised local constant estimator (SSLCE), based on the local constant estimator (LCE), and reveal the asymptotic properties of SSLCE. We also show that the SSLCE has a faster convergence rate than that of the LCE when a well chosen weighting factor is employed. Our experiment with synthetic data shows that the SSLCE can improve performance with unlabeled data, and we recommend its use with the proper size of unlabeled data.

• KCID journal
• /
• v.18 no.1
• /
• pp.25-32
• /
• 2011

In order to effectively manage the reservoir, reservoir water quality management should be based on physicochemical and configurational characteristics. In this research, correlation between factors affecting the reservoir water quality was examined. Chl-a and COD shows the highest positive correlation. Chl-a and T-P also has a high positive correlation, however Chl-a and T-N show lower correlation relatively. Even though T-N is an important factor for phytoplankton growth which increase Chl-a concentration, corelation of Ch1-a and T-N shows that enough nitrogen in the reservoir isn't no longer limiting factor. The age of reservoir can cause of increasing COD and SS. Embankment height and elevation of reservoirs shows strong negative correlation to water quality. That means reservoir which is higher embankment height and locate in higher elevations is less contaminated. Regression expression was derived with Chl-a and water quality parameters, and height of reservoir. Finally Chl-a was simulated using regression expression and it was a good approach to predict the Chl-a concentration.

• Korean Management Science Review
• /
• v.27 no.3
• /
• pp.99-115
• /
• 2010

We classified quality management practices of manufacturing companies into 'Infra' factor and 'Process' factor which were introduced based on existing literatures and previous studies related to quality management. It was our first objective to confirm its conformance in Korean manufacturing industry, and to examine the two factors effect positively on production performance. Moreover, we tried to examine whether JIT and workforce management have an moderate effect on 'Infra and Process factors-performance' relationship, using moderate regression. As the result of empirical study on nationwide manufacturing companies, we had the results that the classification between Infra factor and Process factor in quality management practices was valid in Korean manufacturing companies, and that the two factors gave performances positive effect. Moreover, our secondary analysis showed that JIT had an positive moderate effect on the Infra factor while workforce management on the Process factor. With these results, we have an interpretation that manufacturing companies which aim to enhance short term performances have to concentrate their effort and investment on Process factor and, on the other hand, ones which aims to improve long term performances on Infra factor.