• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.593-601
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• 2013

We often need to include categorial variables as explanatory variables in regression models. The categorial variables in regression models can be quantified through dummy variables. In this study, we provide an education tool using Excel VBA for displaying regression lines along with test results for regression models with a continuous explanatory variable and one or two categorical explanatory variables. The regression lines with test results are provided step by step for the model(s) with interaction(s), the model(s) without interaction(s) but with dummy variables, and the model without dummy variable(s). With this tool, we can easily understand the meaning of dummy variables and interaction effect through graphics and further decide which model is more suited to the data on hand.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.61-83
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• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.

• Korea Journal of Hospital Management
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• v.13 no.4
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• pp.1-26
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• 2008

This study examines the statistical relationship between medical specialists and managerial performance, using regression analysis with the number of medical specialists per 100 beds as the independent variable and the managerial performance index as the dependent variable. Managerial performance index incorporated the number of out-patients per specialist, the number of in-patients per specialist, the volume of revenue per specialist, the number of beds per specialist, and the average length of stay. To compare different groups of hospitals, dummy variable was applied to five groups of hospitals according to size: 100-299 beds, 300-599 beds, 600-899 beds, 900-1199 beds, and more than 1200 beds. The data consisted of 181 general hospitals with more than 100 beds, which included 28 public hospitals, 73 corporate hospitals, 64 university hospitals and 16 private hospitals. Of those, 87 hospitals were located in big cities and 94 hospitals in medium to small cities. This study used hospitals from the Korean Hospital Association, and data published in 2004. The collected data sample was analyzed using the SPSSWIN 12.0 version, and the study hypothesis was tested using regression analysis. The findings of this study are summarized as follows: Hypothesis 1 predicting a negative effect of the number of medical specialists on the number of out-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in all the hospital groups larger than the group of 100-299 beds. Hypothesis 2 predicting a negative effect of the number of medical specialists on the number of in-patients per specialist was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds when compared to the group of 100-299 beds. Hypothesis 3 predicting a negative effect of the number of medical specialists on the volume of revenue per specialist was not supported. However, the analysis of dummy variable showed that the volume of revenue per specialist increased in the hospital groups of 600-899 beds, 900-1199 beds, and over 1200 beds, when compared to the group of 100-299 beds. Hypothesis 4 predicting a negative effect of the number of medical specialists on the average length of stay was supported with statistical significance. The analysis of dummy variable showed causality in the hospital group of 300-599 beds, when compared to the group of 100-299 beds. Results of this study show that the number of the medical specialists per 100 beds is an important factor in hospital managerial performance. Most hospitals have tried to retain as many medical specialists as possible to keep the number of patients stable, to ensure adequate revenue, and to maintain efficient managerial performance. Especially, the big hospitals with greater number of beds and medical specialists have shown greater revenue per medical specialist despite the smaller number of patients per medical specialist. Findings of this study explains why hospitals in Korea are getting bigger.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.140-143
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• 2008

This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.1
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• pp.45-58
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• 2011

Objectives: The purpose of this study is to analyze the fators affecting Internet addiction among adolescents in an area. Methods: By using cluster sampling, 2,479 participants representing 22 elementary school, 11 middle school, 7 high school students in a county of the Chungcheongnam-do. Data was analyzed by SPSS 12.0. using t-test, F-test, chi-square test, Pearson correlation coefficient and multiple regression. Results: Internet addiction positively correlated with high school students(dummy variable), Internet-connected computers in PC Game Room(dummy variable), Internet using time(weekday) and Internet using time(weekend). Internet addiction negatively correlated with Internet-connected computers in school(dummy variable), Internet-connected computers in friend's house(dummy variable). For the male students, Significant factors affecting Internet addiction were eating habits, Internet-connected computers in friend's house, Internet using time(weekend). For the female students, Internet using time(weekday) and Internet using time(weekend) were significant. For the elementary school students, Significant factors affecting Internet addiction were Internet using tine(weekday) and Internet using time(weekend). In the case of the middle school students, Internet using tine(weekday), Internet using time(weekend) and eating habits were significant. but, the high school students, Internet using time(weekend) was significant. Conclusions: Students who spend more time in the internet have higher tendency to become addicted to the internet. Therefore, it would be necessary to develop program to prevent internet addiction.

• Asian Journal of Innovation and Policy
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• v.12 no.1
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• pp.101-130
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• 2023

The increasing cost, and demand for, household energy has increased attention to the phenomena of energy burdens. Despite this increased attention, a lack of consensus remains in pinpointing the strongest predictors, and geographic differences, that exist within the energy ecosystem. This study addresses this gap by utilizing a series of dummy variable regressions across cities, suburbs, and rural areas within Erie County, New York-a county noted to have particularly high energy burdens. Specifically, three types of predictor sets were incorporated into the methodology: a set of socioeconomic variables, physical variables, and a combination of both variable sets. The results of this study suggest that cities tend to have the highest electricity burdens. Despite the aging infrastructure in Erie County, high energy burdens were driven primarily by socioeconomic factors such as housing cost burden and poverty status. Lastly, this study explores various planning and policy implications Erie County can utilize to reduce energy burdens. In turn, this study highlights the importance of focusing policy efforts on existing social service programs to provide support to the region's neediest households.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.124-137
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• 2001

In general, it is very efficient and effective to use screening variables that are correlated with the performance variable in case that measuring the performance variable is impossible (destructive) or expensive. The general methodology for searching surrogate variables is regression analysis. This paper considers the inspection problem in CRT (Cathode Ray Tube) production line, in which the performance variable (dependent variable) is binary type and screening variables are continuous. The general regression with dummy variable, discriminant analysis and binary logistic regression are considered. The cost model is also formulated to determine economically inspection procedure with screening variables.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.2
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• pp.81-96
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• 2011

Objectives: This study was to examine the factors affecting Stress among adolescents in an area. Methods: Data collection was conducted by self-report survey. Survey participants were 1,255 from 11 middle and 7 high school students in a county of the Chungcheongnam-do, who were selected by the cluster sampling from May 2011. The Structural Equation Modeling was employed to investigate the research Model. Results: Tobacco errands variable was found to have a negative casual effect on self-efficacy factor and male dummy variable had a significant positive casual effect on self-efficacy. Self-efficacy had a significant negative casual effect on stress, smoke dummy variable had a statistically significant negative effect on stress and friends who smoke variable were found to have a positive casual effect on stress. Conclusions: In results, it was confirmd that the adolescents experienced more study related stress than other kinds of stress. Second, it was found out that every kind of stress are relevant to emotional problems and the behaviroral problems.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.495-505
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• 2011

This study performs statistical tests for stability of a long-run relationship in the telecommunication market system by identifying the time path of a recursively estimated cointegration parameter. A dummy variable is used to recover stability for the period that the hypothesis of stable cointegration is rejected, and then a proper cointegrating relation is derived. A dummy variable appears to reflect the structural change in the cointegrating relation according to the analytical results for the error correction term.