• Journal of Korean Home Economics Education Association
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• v.13 no.2
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• pp.27-27
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• 2001

The purpose of this study was to examine the influences of 4-value orientations the 5 money spending styles. and the background variables to the desired money management behavior of high school girls The samples were selected from 498 high school girls. Cronbach'α and Multiple regression were used as statistical analysis.The results were summarized as follows : Resulting from multiple regression analysis, the desired money management behavior of high school girls had the positive linear relationships with the variables such as financial satisfaction. goal-oriented value, and 4 money spending styles - security, conspicuous, non-monetary, and unconcerned - in five money spending ,styles. The most influential variable was money spending styles.

• Health Policy and Management
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• v.25 no.4
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• pp.333-342
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• 2015

Background: The purposes of this study is to analysis the differences of the job satisfaction between regular and non-regular workers in hospitals. Methods: The samples used for data analysis are 632 workers of 6 hospitals using a standardized questionnaires in B, C, D, and G provinces. In research methodology, all the data were analyzed with descriptive statistics, t-test, Pearson's correlation, and multiple linear regression analysis. Results: In case of regular workers, communication, working conditions and employee benefit, and education were found to have a significant positive (+) effect on job satisfaction. In case of non-regular workers, empowerment, reward systems, communication, working conditions, and employee benefit had a significant positive (+) effect on job satisfaction. Conclusion: These results showed that hospitals needed to reinforce communication, working conditions and employee benefit to regular and non-regular workers in order to improve job satisfaction. Especially, more empowerment, working conditions, and employee benefit should be given to non-regular workers.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.20-28
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• 2013

Large workspace and strong grasping force are required when a robot manipulates big and/or heavy objects. In that situation, bimanual manipulation is more useful than unimanual manipulation. However, the control of both hands to manipulate an object requires a more complex model compared to unimanual manipulation. Learning by human demonstration is a useful technique for a robot to learn a model. In this paper, we propose an imitation learning method of bimanual object manipulation by human demonstrations. For robust imitation of bimanual object manipulation, movement trajectories of two hands are encoded as a movement trajectory of the object and a force trajectory to grasp the object. The movement trajectory of the object is modeled by using the framework of dynamic movement primitives, which represent demonstrated movements with a set of goal-directed dynamic equations. The force trajectory to grasp an object is also modeled as a dynamic equation with an adjustable force term. These equations have an adjustable force term, where locally weighted regression and multiple linear regression methods are employed, to imitate complex non-linear movements of human demonstrations. In order to show the effectiveness our proposed method, a movement skill of pick-and-place in simulation environment is shown.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5123-5125
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• 2013

Background: Breast cancer is one the most common cause of cancer-related deaths among women worldwide. The aims of this study were to investigate the impact of dietary factors and health status indicators on breast cancer (BC) incidence. Materials and Methods: Risk factor data (RFD) of 89,404 individuals (15-64 years old) were gathered by questionnaire and laboratory examinations through a cross sectional study from the Non-Communicable Disease Surveillance Centre (NCDSC) of Iran. BC incidences of all provinces through 2001-2006 segregated by age and gender were obtained from the Cancer Registry Ministry of Health (CRMH). Results: a significant positive relationship was seen between diabetes mellitus, fish comsupmption, percent of academic education and non-consumption of fruit, and breast cancer in women. However, non fish consumption, percent age illiteracy and taking fruit showed a significant negative relationship with the incidence of breast cancer. In addition, multiple linear regression analysis showed associations among percentage with academic education, fruit consumption and diabetes. Conclusions: We conclude that dietary factors such as fish and furit consumption, dairy products, health status indicators, academic education, and some diseases like diabetes mellitus can affect the BC incidence, although the results of ecologic studies like this must naturally be interpreted with caution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.181-189
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• 2008

The purpose of this study is to comparatively analyze the characteristics and models of traffic accidents by day and nighttime. In pursuing the above, this study gives particular attentions to testing the differences and developing the models (multiple linear and non-linear and Poisson and negative binomial regression) using the data of Cheongju 4-legged signalized intersections. The main results analyzed are as follows. First, the differences between day and nighttime accidents were defined. Second, 12 accident models which are all statistically significant were developed. Finally, the differences between day and nighttime models were comparatively analyzed using the common and specific variables.

• Journal of Environmental Health Sciences
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• v.42 no.4
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• Journal of Preventive Medicine and Public Health
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• v.40 no.6
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• pp.495-504
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• 2007

Objectives : This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. Methods : Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. Results : While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. Conclusions : This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.701-717
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• 2003

The objective of this study was to extensively estimate the air quality trends of the study area by surveying con-centration trends in months or seasons, after analyzing the mass concentration of PM-10 samples and the inorganic lements, ion, and total carbon in PM-10. Also, the study introduced to apply the PMF (Positive Matrix Factoriza-tion) model that is useful when absence of the source profile. Thus the model was thought to be suitable in Korea that often has few information about pollution sources. After obtaining results from the PMF modeling, the existing sources at the study area were qualitatively identified The PM-10 particles collected on quartz fiber filters by a PM-10 high-vol air sampler for 3 years (Mar. 1999∼Dec.2001) in Kyung Hee University. The 25 chemical species (Al, Mn, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, Pb, Si, N $a^{#}$, N $H_4$$^{+}$, $K^{+}$, $Mg^{2+}$, $Ca^{2+}$, C $l^{[-10]}$ , N $O_3$$^{[-10]}$ , S $O_4$$^{2-}$, TC) were analyzed by ICP-AES, IC, and EA after executing proper pre - treatments of each sample filter. The PMF model was intensively applied to estimate the quantitative contribution of air pollution sources based on the chemical information (128 samples and 25 chemical species). Through a case study of the PMF modeling for the PM-10 aerosols. the total of 11 factors were determined. The multiple linear regression analysis between the observed PM-10 mass concentration and the estimated G matrix had been performed following the FPEAK test. Finally the regression analysis provided source profiles (scaled F matrix). So, 11 sources were qualitatively identified, such as secondary aerosol related source, soil related source, waste incineration source, field burning source, fossil fuel combustion source, industry related source, motor vehicle source, oil/coal combustion source, non-ferrous metal source, and aged sea- salt source, respectively.ively.y.

• Ocean and Polar Research
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• v.26 no.3
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• pp.465-473
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• 2004

Five parameters such as the entrance size of the front wall, conduit size, wave period, wave height and the width of water pool were selected to estimate the inflow rate, which is basic and essential input data to design seawater exchange breakwater with a submerged mound by conducting hydraulic model experiments. In the results of multiple regression analysis, log-log equation showed a good agreement rather than linear equation and the estimation of inflow rate was well done with only two parameters except entrance size of the front wall, wave period and the width of water pool. Finally, non-dimensional flow rate equation is derived.