• Journal of Soil and Groundwater Environment
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• v.13 no.5
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• pp.57-73
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• 2008

Nitrate concentrations were measured up to 49 mg/L (as $NO_3$-N) and 22% of the samples exceeded drinking water standard in shallow and bedrock groundwater of the northern Nonsan area. Nitrate concentrations showed a significant difference among land use groups. To predict nitrate concentration in groundwater, multiple regression analysis was carried out using hydrogeologic parameters of soil media, topography and land use which were categorized as several groups, well depth and altitude, and field parameters of temperature, pH, DO and EC. Hydrogeologic parameters were quantified as area proportions of each category within circular buffers centering at wells. Regression was performed to all the combination of variables and the most relevant model was selected based on adjusted coefficient of determination (Adj. $R^2$). Regression using hydrogelogic parameters with varying buffer radii show highest Adj. $R^2$ at 50m and 300m for shallow and bedrock groundwater, respectively. Shallow groundwater has higher Adj. $R^2$ than bedrock groundwater indicating higher susceptibility to hydrogeologic properties of surface environment near the well. Land use and soil media was major explanatory variables for shallow and bedrock groundwater, respectively and residential area was a major variable in both shallow and bedrock groundwater. Regression involving hydrogeologic parameters and field parameters showed that EC, paddy and pH were major variables in shallow groundwater whereas DO, EC and natural area were in bedrock groundwater. Field parameters have much higher explanatory power over the hydrogeologic parameters suggesting field parameters which are routinely measured can provide important information on each well in assessment of nitrate contamination. The most relevant buffer radii can be applied to estimation of travel time of contaminants in surface environment to wells.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.95-106
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• 2016

Air transportation demand is consistently increasing in Korea due to economic growth and low cost carriers. For this reason, airport expansion plans are being discussed in Korea. Therefore, it is essential to forecast reliable air transportation demand with adequate methods. However, most of the air transportation demand models in Korea has been developed by simple regression analysis with several dummy variables. Simple regression analysis without considering stationarity of time-series data can bring spurious outputs when a direct causal relationship between explanatory variables and dependent variable does not exist. In this paper, econometric model were developed for air transportation demand based on stationarity in time-series data. Unit root test and co-integration test are used for testing hypothesis of stationarity.

• Journal of Distribution Science
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• v.12 no.4
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• pp.31-39
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• 2014

Purpose - This study investigates the following two issues. First, we attempt to find the important determinants of housing investment and to identify their significance rank using survey panel data. Recently, the expansion of global uncertainty in the real estate market has directly and indirectly influenced the Korean housing market; households demonstrate a sensitive reaction to changes in that market. Therefore, this study aims to draw conclusions from understanding how the impact of financial strength of the household is related to house investment. Second, we attempt to verify the effectiveness of diverse indices of financial strength such as DTI, LTV, and PIR as measures to monitor the housing market. In the continuous housing market recession after the global crisis, the government places top priority on residence stability. However, the government still imposes forceful restraints on indices of financial strength. We believe this study verifies the utility of these regulations when used in the housing market. Research design, data, and methodology - The data source for this study is the "National Survey of Tax and Benefit" from 2007 (1st) to 2011 (5th) by the Korea Institute of Public Finance. Based on this survey data, we use panel data of 3,838 households that have been surveyed continuously for 5 years. We sort the base variables according to relevance of house investment criteria using the decision tree model (DTM), which is the standard decision-making model for data-mining techniques. The DTM method is known as a powerful methodology to identify contributory variables for predictive power. In addition, we analyze how important explanatory variables and the financial strength index of households affect housing investment with the binary logistic multi-regressive model. Based on the analyses, we conclude that the financial strength index has a significant role in house investment demand. Results - The results of this research are as follows: 1) The determinants of housing investment are age, consumption expenditures, income, total assets, rent deposit, housing price, habits satisfaction, housing scale, number of household members, and debt related to housing. 2) The impact power of these determinants has changed more or less annually due to economic situations and housing market conditions. The level of consumption expenditure and income are the main determinants before 2009; however, the determinants of housing investment changed to indices of the financial strength of households, i.e., DTI, LTV, and PIR, after 2009. 3) Most of all, since 2009, housing loans has been a more important variable than the level of consumption in making housing market decisions. Conclusions - The results of this research show that sound financing of households has a stronger effect on housing investment than reduced consumption expenditures. At the same time, the key indices that must be monitored by the government under economic emergency conditions differ from those requiring monitoring under normal market conditions; therefore, political indices to encourage and promote the housing market must be divided based on market conditions.

• Management & Information Systems Review
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• v.34 no.5
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• pp.71-92
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• 2015

This study analyzes the impact of Corporate Social Responsibility (CSR) activities on financial performances using Bayesian Network. The research tries to overcome the issues of the uniform assumption of a linear function between financial performance and CSR activities in multiple regression analysis widely used in previous studies. It is required to infer a causal relationship between activities of CSR which have an impact on the financial performances. Identifying the relationship would empower the firms to improve their financial performance by informing the decision makers about the different CSR activities that influence the financial performance of the firms. This research proposes General Bayesian Network (GBN) and presents Markov Blanket induced from GBN. It is empirically demonstrated that all the proposals presented in this study are statistically significant by the results of the research conducted by Korean Economic Justice Institute (KEJI) under Citizen's Coalition for Economic Justice (CCEJ) which investigated approximately 200 companies in Korea based on Korean Economic Justice Institute Index (KEJI index) from 2005 to 2011. The Bayesian Network to effectively infer the properties affecting financial performances through the probabilistic causal relationship. Moreover, I found that there is a causal relationship among CSR activities variable; that is Environment protection is related to Customer protection, Employee satisfaction, and firm size; Soundness is related to Total CSR Evaluation Score, Debt-Assets Ratio. Though the what-if analysis, I suggest to the sensitive factor among the explanatory variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.560-565
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• 2016

In this study, we empirically scrutinize the relationship between short selling transactions and stock price behaviors using the stock market data in Korea during the period from January 2005 to March 2016. We chose the short selling volume ratio (SVR), stock lending volume ratio (LVR), and stock lending open interest ratio (LIR) as variables of the short selling trading activities. We construct portfolios based on the percentile of the short selling volume ratio during the sample period; upper-10%-SVR portfolio, upper-25%-SVR portfolio, upper-50%-SVR portfolio. We estimate the monthly firm-specific return and monthly skewness of the daily firm-specific returns of each portfolio. The firm-specific return or skewness is specified as a dependent variable and the short selling activities as explanatory variables. The results show that all of the statistically significant estimates of the short selling activities for the firm-specific returns are negative and that all of the statistically significant estimates of the skewness of the short selling activities are positive. These results support the hypothesis that short selling activities cause the stock price to decrease.

• Journal of Gifted/Talented Education
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• v.21 no.4
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• pp.961-977
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• 2011

The purpose of this study was to investigate the relationship between learning motivation and task commitment and find sub factors of learning motivation that affect task commitment. For this study 30 science gifted student (4th and 5th grade in elementary school) participated. The survey instruments used for this study were Academic Motivation Scale and Task Commitment Scale. The statistical methods employed for data analysis were the correlation analysis and multiple regression analysis. The result of this study were as follows: First, the learning motivation and task commitment of science gifted students showed similar levels. But there was differences of strength each sub factors of learning motivation and task commitment. Second, there was a significant positive correlation between learning motivation and task commitment. Also, learning motivation has the explanatory power of predictive variable for the task commitment approximately 49.3%. Expecially learning motivation has significant positive correlation with responsibility and self-control that sub factors of task commitment. Among the sub factor of learning motivation, confidence has most correlations with sub factors of task commitment and significant impact on task commitment. This result indicate that we need to develop learning motivation to improve task commitment and especially develop learning motivation program to grow up confidence of science-gifted.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1131-1141
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• 2012

In this study, factors that influence water supply capacities of dams are extracted using factor analysis, and multiple regression equations for estimating water supply capacities of dams are developed using the analysis results. Twenty-one multi-purpose dams and twelve Municipal and Industrial (M&I) water supply dams are selected for case studies, and eight variables influencing water supply capacities of dams, namely: watershed area, inflow, effective reservoir storage, grade on amount of M&I water supply, grade on amount of agricultural water supply, grade on amount of in-stream flow supply, grade on river administration, and grade on average rainfall, are determined. Two case studies for multi-purpose dams and M&I water supply dams are performed, employing factor analysis, respectively. For the two cases, preliminary tests, such as reviewing matrix of correlation coefficient, Bartlett's test of sphericity, and Kaiser-Meyer-Olkin (KMO) test, are conducted to evaluate the suitability of the variables for factor analysis. In case of multi-purpose dams, variables are grouped into three factors; M&I water supply dams, two factors. The factors are rotated using Varimax method, and then factor loading of each variable is computed. The results show that the variables influencing water supply capacities of dams are reasonably selected and appropriately grouped into factors. In addition, multiple regression equations for predicting the amounts of annual water supply of dams are established using the factor scores as explanatory variables, it is identified that the models' accuracies are high, and their applications to determining effective storage capacity of a dam during dam planning and design steps are presented. Consequently, it is thought that the variables and factors are useful for dam planning and dam design.

• Research in Community and Public Health Nursing
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• v.11 no.1
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• pp.37-66
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• 2000

Volunteers activities in Health Centers are strategically important for the efficient utilization of human resource and local people's health promotion in community. With these backgrounds. this study is conducted to examine significant factors in relation to demand and utilization of volunteers in Health Centers. and the factors are the characteristics of Health Centers. volunteer management factors and external environment factors. Subjects in this study were 245 Health Centers all chosen. Data were collected from April. 12. 1999 to May. 31. 1999. and the data for analyses were ones of 116 respondents. Then. the data coded and submitted to Fisher's exact test. NPAR1WAY ANOVA, Correlation analysis. multiple regression analysis, multiple logistic regression analysis with SAS program. The key results from this study can be epitomized as follows: 1. 43.1% of responding health centers answered that they 'utilize volunteers'. The average number of volunteers who were engaged in responding health centers was 43, out of which 7 were men and 36 were women. As for the adequacy of the number of the volunteers. 55.1% responded 'not enough' and 30.6% responded 'adequate'. The more the number of volunteers needed. the more the number of utilizing volunteers is. When asked about their views concerning the utilization of volunteers in Health Centers. 88.7% of all respondents answered in the affirmative. The accountable factor for the utilization of volunteers was the present utilization of volunteers. 2. Concerning the reasons for using volunteers. 'to induce local people's participation in health services' was the highest comprising 76% of the responding health centers. 3. Most of volunteers were housewives and independent enterprisers. The most type of volunteer activities was 'just simple labor'. 4. As for the action duration of volunteers. 69.4% answered 'under 6 months'. The factor was significant difference with the action duration of volunteers was 'to provide social meeting' in the middle of rewards for volunteers. 5. Asked about the problem in utilizing volunteers. 53.2% answered 'the difficulty of recruitment and education for volunteers'. and 42.6% answered 'lack of budget and manpower needed for the utilization of volunteers.' 6. Concerning the evaluation of the performance by volunteers. 88% answered 'satisfactory'. With regards to the reason for that. 29.3% answered 'volunteers can provide various kinds of services' 7. 88.7% of responding health centers answered that they will continuously or newly utilize volunteers in the future. 8. The main health program services which expect utilization of volunteers were visiting health services(63.2%). old people's health services (25.3%). These were not significant difference with any explanatory variable. 9. The average number of volunteer needed in responding health centers was 38. The more the average number of utilizing volunteers. the more the number of volunteers needed is. The more the degree of financial independence. the more the number of volunteers needed is. In conclusions. Health centers are necessary to promote their role of recruitment. education and training for volunteers. the development of volunteer activities programs.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.373-396
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• 2015

This study analyzed the determinants of high-growth firms using the technology appraisal data of the Korea Technology Finance Corporation. This study is differentiated from previous studies for three reasons. First, it analyzed the determinants of firms that will grow into high-growth firms in the future, not the characteristics of current high-growth firms. Second, it analyzed high-growth firms by dividing them in two aspects: sales and employment. In other words, they were divided into three types: the case in which a firm achieves high growth in both sales increase and creation of jobs, the case in which a firm achieves high growth in creation of jobs but low growth in sales increase, and the case in which a firm achieves high growth in only sales increase but low growth in creation of jobs. Third, this study applied the technology appraisal indicators of Kibo Technology Rating System(KTRS) by the Korea Technology Finance Corporation as the explanatory variable. As a result of analysis, it was found that a firm achieved high growth in both sales and employment if the position in the technology life cycle was appropriate and the technology readiness level was high. However, it turned out that the management system of technical manpower had conflicting effects on high growth of employment and sales. In other words, a firm that had well managed its technical manpower achieved high growth in terms of employment, but rather showed low growth in terms of sales. This result suggests the inference that firms showing high growth in employment may appear mainly in the high-tech industry where management of technical manpower is important. Accordingly, as a result of adding dummy variables that represent whether or not firms are in the high-tech industry, it was found that the result supported the inference, as firms in the high-tech industry were highly likely to achieve high growth in employment.