• 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.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.435-442
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• 2009

Multiple genes interacting is a difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction(MDR) statistical method by dummy variables was applied to identify interaction effects of single nucleotide polymorphisms(SNPs) responsible for longissimus mulcle dorsi area(LMA), carcass cold weight(CWT) and average daily gain(ADG) in a Hanwoo beef cattle population.

• Journal of Korean Society of Forest Science
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• v.94 no.2 s.159
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• pp.121-126
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• 2005

This study was carried out to construct a single diameter and a single height model that could localize Chamaecyparis obtusa stand grown in 3 Southern regions of Korea. Dummy variables, which convert qualitative information such as geographical regions into quantitative information by means of a coding scheme (0 or 1), were used to localize growth models. In results, modified form of Gompertz equation, $Y_2={\exp}({\ln}(Y_1){\exp}(-{\beta}(T_2-T_1)+{\gamma}({T_2}^2-{T_1}^2))+({\alpha}+{\alpha}_1Al+{\beta}_1k_1+{\beta}_2k_2)(1-{\exp}(-{\beta}(T_2-T_1)+{\gamma}({T_2}^2-{T_1}^2))$, for diameter and height was successfully disaggregated to provide different projection equation for each of the 3 regions individually. The use of dummy variables on a single equation, therefore, provides potential capabilities for testing the justification of having different models for different sub-populations, where a number of site variables such as altitude, annual rainfall and soil type can be considered as possible variables to explain growth variation across regions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.287-297
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• 2019

When cryptographic algorithms are implemented to provide countermeasures against the side-channel analysis, designers frequently employ the combined countermeasures between the first-order masking scheme and hiding schemes. Their combination can be enough to offer security and efficiency. However, if dummy operations can be distinguished from real operations, an attacker can extract the secret key with lower complexity than the intended attack complexity by the designer inserting the dummy operations. In this paper, we categorize types of variables used in a dummy operation when C language is employed. Then, we present the novel vulnerability that can distinguish dummy operations for all cases where the hiding schemes are applied using different types of variables. Moreover, the countermeasure is provided to prevent the novel vulnerability.

• Journal of the Korean housing association
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• v.10 no.2
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• pp.235-245
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• 1999

The purpose of the study is to examine the effects of household characteristics on housing expenditure. The data from the National Survey of Family Income and Expenditure 1996 were used for the analysis of this study, and the final sample included 12,323 households. It was found that total housing expenditure was significantly different according to the tenure type, household income, household size, age, occupation and education of the head, or location of housing. The significantly explanatory variables in the model of total housing expenditure were owner and yearly-renter dummy, household income and the household income squared, mortgage-off dummy, Seoul and metropolitan city dummy, and employed-wife dummy.

• 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.

• 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.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.197-202
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• 2010

This study deals with the accident models of arterial link sections by driving type. The objectives is to develop models by driving type using the accident data of 24 arterial links in Cheong-ju. In pursuing the above, this study gives particular emphasis to modeling such the accidents as the straight, lane change and others. The main results analyzed are as follows. First, the number of accidents is analyzed to account for about 59% in straight, 31% in lane change and 10% in others. Second, the number of left-turn lane as common variables, and the ADT, number of pedestrian crossings, connecting roads and link length as specific variables are selected in developing models(number of accident and EPDO). Third, 8 models which are all statistically significant are developed. Finally, RMSE of the driving type models was analyzed to be better than that of dummy variable.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.601-604
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• 2006

This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.