• Korea Journal of Hospital Management
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• v.27 no.2
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• pp.44-52
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• 2022

Purposes: This study tried to extract variables affecting patient-experience satisfaction level in hospital situation, using a multiple-regression analysis and ISA(Revised Importance-Satisfaction Analysis), and to explore variables needed to be improved. Methodology: A mobile-based online patient-experience survey was conducted in eleven general hospitals in A city. To test the validity of this test, this data was compared with the data from Health-Insturance Review and Assessment Service. Then, the standardized regression coefficients extracted from a multiple-regression analysis were used as the importance scale to be used in ISA. Finding: Taken together, the areas with the highest contribution for the in-hospital patient-experience satisfaction level were medication and treatment process and hospital environment. In conclusion, the revised ISA which can show satisfaction and importance both with simultaneously and multi-axis way would be useful in hospital improvement activities. Practical Implications: This study tried to develop a mobile-based patient-experience survey, and to extract the major variables affecting patient-satisfaction level and to identify variables need to be improved. Finally, this should help hostipals to prepare the assessment process with various improvement activities.

• Journal of Industrial Technology
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• v.26 no.A
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• pp.119-127
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• 2006

The purpose of this study is to present quantitatively the influence of variables that had the largest effect on the changes in suspended solids(SS), which would cause turbid water phenomenon, among water quality factors of the non-point pollution source, and then to develop a multiple regression equation of SS and predict the water quality of ungaged watersheds so as to provide basic data to establish efficient management plans for SS which flow in rivers and lakes. To identify the correlation of SS with the amount of rainfall and the state of land use, a simple correlation analysis and a simple regression analysis were conducted respectively. Finally, a multiple regression analysis was conducted to provide that SS were set as dependent variables while the amount of rainfall, paddy fields and dry fields were set as independent variables. As a result, the amount of rainfall had the most significant influence on changes in SS, followed by dry fields and paddy fields. In addition, the multiple regression equation was developed to predict SS in unmeasurable watersheds.

• Korean Journal of Child Studies
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• v.32 no.3
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• pp.19-41
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• 2011

This paper was an examination of gender-specific behaviors in children and the types of bullying behavior among 1,181 fifth and sixth grade elementary schools student identified were then classified. Differences were identified in individual variables, family variables, and school variables. The data thus collected were subjected to descriptive and comparative statistical analysis using the SPSS software program. Our results showed that multiple discriminant analysis yielded a function of individual, family and school variables that proved effective in classifying bully, reinforcer, assistant, victim, outsider and defender types in boys. In girls, multiple discriminant analysis yielded a function of individual variables that was effective in classifying bully, reinforcer, assistant, victim, outsider and defender types.

• Earthquakes and Structures
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• v.7 no.6
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• pp.1061-1070
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• 2014

An iterative hybrid structural dynamic reliability prediction model has been developed under multiple-time interval loads with and without consideration of stochastic structural strength degradation. Firstly, multiple-time interval loads have been substituted by the equivalent interval load. The equivalent interval load and structural strength are assumed as random variables. For structural reliability problem with random and interval variables, the interval variables can be converted to uniformly distributed random variables. Secondly, structural reliability with interval and stochastic variables is computed iteratively using the first order second moment method according to the stress-strength interference theory. Finally, the proposed method is verified by three examples which show that the method is practicable, rational and gives accurate prediction.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.161-166
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• 2012

This study deals with the accident of roundabout. The objective is to analyze the traffic accidents occurred in 3-legged and 4-legged roundabouts through the developed models. In developing the multiple linear regression models, this study uses the number of traffic accidents as a dependent variable and such the variables as geometric structures, traffic characters and others as the independent variables. The correlation and multicollinearity of variables were analyzed using SPSS17.0. The main results are as follows. First, R-square value of developed models were analyzed to be 0.851(3-leg) and 0.689(4-leg), respectively. Second, the independent variables in the 3-legged roundabout accident model were analyzed to be the traffic volume and number of crosswalk, and the variables in the 4-legged roundabouts were evaluated to be the traffic volume and signal. Finally, the paired t-test shows that the predicted values and observed values are not statistically different.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.159-167
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• 2003

This study suggests the hybrid fault diagnosis method of signed digraph (SDG) and partial least squares (PLS). SDG offers a simple and graphical representation for the causal relationships between process variables. The proposed method is based on SDG to utilize the advantage that the model building needs less information than other methods and can be performed automatically. PLS model is built on local cause-effect relationships of each variable in SDG. In addition to the current values of cause variables, the past values of cause and effect variables are inputted to PLS model to represent the Process armies. The measured value and predicted one by dynamic PLS are compared to diagnose the fault. The diagnosis example of CSTR shows the proposed method improves diagnosis resolution and facilitates diagnosis of masked multiple-fault.

• Journal of Environmental Science International
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• v.24 no.11
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• pp.1443-1450
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• 2015

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The $R^2$ value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ${\pm}5%$ error.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.117-137
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.12
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• pp.43-53
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• 1997

In this paper, the optimal synthesis algorithm of multiple-valued logic circuits using universal logic modules (ULM) U$_{f}$ based on 3-variable ternary reed-muller expansions is presented. We check the degree of each varable for the coefficients of reed-muller expansions and determine the order of optimal control input variables that minimize the number of ULM U$_{f}$ modules. The order of optimal control input variables is utilized the realization of multiple-valued logic circuits to be constructed by ULM U$_{f}$ modules based on reed-muller expansions using the circuit cost matrix. This algorithm is performed only unit time in order to search for the optimal control input variables. Also, this algorithm is able to be programmed by computer and the run time on programming is O(p$^{n}$ ).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.98-105
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• 2019

In the past 30 years, our aviation demand has been growing continuously. As such, the importance of the demand forecasting field is increasing. In this study, the factors influencing Korea's international air demand were selected, and the international air demand was analyzed, forecasted and reviewed through OLS multiple regression analysis. As a result, passenger demand was affected by GDP per capita, oil price and exchange rate, while cargo demand was affected by GDP per capita and private consumption growth rate. In particular, passenger demand was analyzed to be sensitive to temporary external shocks, and cargo demand was more affected by economic variables than temporary external shocks. Demand forecasting, OLS multiple regression analysis, passenger demand, cargo demand, transient external shocks, economic variables.