• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.491-498
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• 2013

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.

• International Commerce and Information Review
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• v.11 no.2
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• pp.111-126
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• 2009

This study analysed determinants of Foreign Direct Investment to ASEAN+ 3 member nations using panel data for which cross-sectional data are combined with time series data. The data for the analysis included the amount of FDI, GDP, and indexes of economic independence. This study collected data from six nations(Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam) whose data were easily available, China and Japan from 2003 to 2007 and analysed them. The results are summarized as follows: Using the pooled OLS method, we found Model 2 had the highest explanatory power whose adjusted R-squared was 89.4%, which accounted for about 89% of foreign investment. Using the fixed effect model, Model 2 had the highest explanatory power whose adjusted R-squared was 96.8%, which accounted for about 97% of foreign investment. Using the probability effect model, Model 5 had the highest explanatory power, but in respect to its statistical significance, only GDP was 1% significant and the rest variables had no significance.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.351-359
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• 2013

This study was conducted to investigate the factors that affect the length of stay in hospital for schizophrenic patients. Of the data from the in-depth injury patient surveillance system, the final subject included 2,239 patients with schizophrenia in their final diagnosis. Using SPSS 18.0, a hierarchical regression analysis was performed by sequentially entering the explanatory variables by setting sociodemographic characteristics, discharge characteristics and hospital characteristics as explanatory variables and the length of stay in hospital as a dependent variable. The findings showed that the sociodemographic characteristics had the highest explanatory power and the explanatory power changed when the explanatory variable of the hospital characteristics was added, as opposed to the discharge characteristics. Male, type-1 medicaid, Chungcheong-do and the number of beds were found to be the factors that mostly affect the length of stay. Since this study used the secondary data, it has a limitation in terms of additional variables that could better explain the length of stay for schizophrenic patients. Nevertheless, it has an implication in that it investigated a large scale of data on a national level. For the effort of reducing the length of stay, it is suggested that an effort should be made at the national level, by focusing more on the hospital characteristics as well as the individual characteristics of patients.

• Journal of Korean Academy of Nursing
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• v.30 no.4
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• pp.836-846
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• 2000

This Meta-Analysis of 18 studies was conducted to determine the magnitude of th relationship between health promotion behavior and each of explanatory variables. The studies were measured using Health Promoting Life Style(HPLP) developed by Walker and others based on Pender's definiton of health promoting behavior. The sample was collected by searching for The Journal of Korean Academy Nursing Society, The Journal of Korean Women's Health Nursing Academic Society,The Journal of Korean Academic Society of Adult Nursing, Journal of Korean Community Nursing, The Journal of Fundamentals of Nursing, The Journal of Korean Nursing Administration Academic Society, The Korean Journal of Child Health Nursing, The Journal of Korean Psychiatric Academic Society, the dissertations for mater degree or doctoral dissertations for the period from 1980 to 1998. The explanatory variables measured more than 2 times in studies were self-efficacy, perceived health status, self-esteem, internal, powerful-others and chance dimensions of health locus of control, perceived benefits, hardiness, wellbeing and clinical demensions of health concepts, and quality of life(life satisfaction). Effect sizes were calculated by unweighted mean r, weighted mean r by sample size and weighted mean r by quality index score after homogeneity test. The mean r effect size indicator range of each predictor variable were as follows; quality of life (0.50- 0.52), self-efficacy (0.46-0.47), hardiness (0.42-0.44), self-esteem(0.41-0.43), health locus of control- internal(0.32-0.34), health locus of control- powerful others (0.25-0.31), perceived health status(0.18-0.19) and clinical dimensions of health concepts (0.16-0.17).

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.139-151
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• 2014

This paper analyzes factors affecting rail transit ridership at urban rail stations of the Daegu Metropolitan City in 2011. Rail transit ridership is analyzed by dividing weekdays and weekends in order that their differences may be observed. The data used in this study includes various explanatory variables, such as floor area which was collected from building ledger and GIS cadastral map, number of bus routes(line) possible to transfer from urban rail transit, number of students enrolled in middle and high schools, and universities located in access areas of rail transit. For this study, multiple regression models are estimated including various explanatory variables affecting rail transit ridership of weekdays and weekends. From the study, the number of statistically significant explanatory variables and the relative effect of each variable are shown to be different between weekdays and weekends.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.743-753
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• 2014

Kim et al. (2006) and Kim et al. (2009) reported a set of explanatory variables affecting donation frequency when they analyzed nationwide survey data on donations collected in 2002 by Volunteer 21, a nonprofit organization in Korea. The primary purpose of this paper is to correct computational errors found in Kim et al. (2006) and Kim et al. (2009), to rectify major results in the Tables and Figures and to supplement Kim et al. (2009) by providing new results. We add two logistic regressions to the ZIP and a mixture of two Poisson regressions of Kim et al. (2009). Through these two logistic regressions we could detect a set of explanatory variables affecting donation activity (0 or 1) and another set of explanatory variables, in which the volunteer (0, 1) variable is common, discriminating the infrequent donor group from the frequent donor group.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.403-410
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• 2014

Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.179-191
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• 2019

It is not easy to predict the return on investment in the content business, and there is no index to evaluate cast & crew. The absolute number of TV ratings is steadily declining, but there is no substitute index yet. In this study, we tried to predict the relative popularity of the drama by designing the relative superiority of the individual drama viewership as the response variable and designing the relative superiority of the drama participants as the explanatory variables. We used various machine learning algorithms and added explanatory variables that were found to be useful in previous studies. As a result, with properly combined explanatory variables, a high prediction accuracy of 84% is obtained. In this study, we intend to promote the investment efficiency of the entire contents industry by predicting the relative popularity of the contents.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.179-190
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• 2009

In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.1
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• pp.109-122
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• 2000

The present study examined the causal relationships among nurses' perceived autonomy, job satisfaction, work environment (work overload, role conflict, situational support, head nurses' leadership), personal aspects(experiences, need for achievement, professional knowledge and skill) by constructing and testing a theoretical framework. Based on literature review nurses' perceived autonomy and job satisfaction were conceived of as outcomes of the interplay among work environment and personal characteristics. Work environment factors involved work overload, role conflict, situational support, and head nurses' leadership (task oriented leadership, relation oriented leadership). Personal charateristics included experiences, need for achievement, and professional knowledge and skill. Three large general hospital in Chonbuk were selected to participate. The total sample of 516 registered nurses represents a response rate of 92 percent. Data for this study was collected from July to September in 1998 by Questionnaire. Path analyses with LISREL 7.16 program were used to test the fit of the proposed conceptual model to the data and to examine the causal relationship among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however, that path analisis can not count measurement errors; measurement error can attenuate estimates of coefficient and explanatory power. Nevertheless the model revealed relatively high explanatory power. 42 percent of nurses' perceived autonomy was explained by predicted variables; 32 percent of nurses' job satisfaction was explained by by predicted variables. Tn predicting nurses' perceived autonomy the findings of this study clearly demonstrated the work overload might be the most important variable of all the antecedent variables. Head nurses' relation oriented leadership, situational supports, need for achievement, and role conflict were also found to be important determinants for nurses' perceived autonomy. As for the job satisfaction, role conflict, situational supports, need for the achievement, and head nurses' relation oriented leadership were in turn important predictors. Unexpectedly the result showed perceived autonomy have few influence on job satisfaction. The results were discussed, including directions for the future research and practical implication drawn from the research were suggested.