• 통상정보연구
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• 제11권2호
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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.

• 수산해양교육연구
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• 제27권4호
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• pp.998-1012
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• 2015

This study is smartphone addiction impulsiveness, stress, self-efficacy, and examine any changes to appear self-control. This study is a response to the results obtained for 310 people targeting high school in Pusan, the second grade students. For the analysis of the collected data by using the SPSS 22.0 program was the analysis of the T-test, ANOVA, Multiple Regression. The major findings of this study can be summed up as follows: first, smart phone addiction has significant difference in impulsivity, stress, self-efficacy, and self-control. Second, sex is found to be significant in impulsivity, stress, self-efficacy, and self-control. Third, grades are significant in impulsivity, self-efficacy, and self-control. Fourth, the model for impulsivity indicates 4% of explanatory power, which is significant. Fifth, explanatory power for stress is 4%, which is significant. Sixth, the model for self-efficacy shows 14% of explanatory power, which is significant. Meanwhile, smart phone addiction, sex, and grades have no significant effects on self-efficacy. Seventh, the model for self-control indicates 20% of explanatory power, which is significant.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.263-273
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• 1998

In this paper, we consider some approximate testings for the reliability of the stress-strength model when the stress X and strength Y each depends linearly on some explanatory variables z and w, respectively. We construct a bootstrap procedure for testing for various values of the reliability and compare the power of the bootstrap test with the test based on Mann-Whitney type estimator by Park et.al.(1996) for small and moderate sample size.

• 디지털융복합연구
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• 제19권5호
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• pp.241-247
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• 2021

본 연구는 1990년도부터 2019년까지 거래소에 상장된 기업들을 대상으로 발생액 추정모형의 설명력 변화를 분석하였다. 기존의 발생액 추정모형에 사용된 재무적 변수들이 특성이 시간이 지남에 따라 변화하거나, 전체 발생액 중에서 재량적 발생액의 비중이 변화하면 모형의 설명력에도 변화가 있을 것으로 기대하고 이를 가설화하여 분석하였다. 회귀 분석결과 수정 Jones 모형(1995)은 시간의 경과에 따라 그 설명력이 점차 낮아짐을 발견하였다. 이는 발생액 자체의 증가와 모형에 포함된 변수들의 분포가 변화함에 기인하는 것으로 추정된다. 본 연구의 시계열적 분석 결과는 이익조정 연구 등 학술적인 면이나 회계 정보를 이용하는 이용자에게 중요한 시사점을 제공할 것으로 기대된다.

• 한국학교ㆍ지역보건교육학회지
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• 제10권2호
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• pp.15-27
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• 2009

Background and Goals: This study set out to apply the Theory of Planned Behavior (TPB), which is known to provide good explanations about human behavior, and test it to see if it could predict safety behavior by affecting the intention for safety behavior and perceived behavioral control and if intention for safety behavior would be influenced by attitude toward behavior, subjective norm, and perceived behavioral control. Methods: The subjects were 98 dental technology majors in D City. The questionnaires were distributed, filled out and collected on the spot. Each item was measured on a seven-point scale, and it's interpreted that the higher mean of each item would translate into safety behavior. Results: The analysis results of the Theory of Reasoned Action (TRA) variables indicate that only subjective norm ($\beta$ = .528, p < .000) had explanatory power of 27.2% (F = 37.170, P <.001) for intention for safety behavior. The results show that subjective norm and attitude toward behavior affect intention for safety behavior. The analysis results of the TPB variables revealed that intention for safety behavior had explanatory power of 26.6% (F = 36.072, p <.000) for behavior. When intention was added by perceived behavioral control, the explanatory power increased to 34.5% (F = 26.530, p <.000). And when it's added by knowledge, the explanatory power increased to 39.0% (F =21.661, p <.000). The results suggest that intention has the biggest influence on predicting safety behavior. Conclusion: The results show that the TPB model by Ajzen (1985) has greater forecasting power for intention and act of safety behavior than the TRA model by Fishbein & Ajzen (1980) and the TPB model can applied in the prediction of safety behavior. Thus safety behavior is considered as behavior whose determination control is limited. And safety education programs that add knowledge to the TPB variables will help the students promote their safety behavior.

• 한국항만학회지
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• 제14권2호
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• pp.175-185
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• 2000

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the purpose of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the rap junctions of freeway with 70mph speed limit. From the traffic analyses, and model construction and verification for delay prediction on the ramp junctions of freeway, the following results were obtained : ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junctions of freeway. ⅲ) The delay-occupancy curve showed a remarkable shift based on the occupancies observed : O$\_$d/〈9% and O$\_$d/$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under O$\_$d/$\geq$9%, but lowly for delay prediction on the ramp junctions of freeway under O$\_$d/〈9%. Rather, the driver characteristics or transportation conditions around the freeway were thought to be a little higher explanatory for the delay prediction under O$\_$d/〈9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• ETRI Journal
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• 제18권2호
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• pp.87-106
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• 1996

This paper proposes a new model as a framework for forecasting demand and technological substitution, which can accommodate different patterns of technological change. This model, which we named, "Adaptive Diffusion Model", is formalized from a conceptual framework that incorporates several underlying factors determining the market demand for technological products. The formulation of this model is given in terms of a period analysis to improve its explanatory power for dynamic processes in the real world, and is described as a continuous form which approximates a discrete derivation of the model. In order to illustrate the applicability and generality of this model, time-series data of the diffusion rates for some typical products in electronics and telecommunications market have been empirically tested. The results show that the model has higher explanatory power than any other existing model for all the products tested in our study. It has been found that this model can provide a framework which is sufficiently robust in forecasting demand and innovation diffusion for various technological products.

• 디지털융복합연구
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• 제7권3호
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• pp.13-24
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• 2009

Technology Acceptance Model (TAM) has been a basis model for testing technology use. Post researches of TAM have been conducted with the updating the TAM by adding new independent variables in order to increase the explanatory power of the model. However, the problem is that different independent variables have to be required to keep the explanatory power whenever adopting particular technology. This might reduce the generality of the research model. Thus in order to increase the generality of the model, this study reviewed the previous researches and collected the independent variables used, and regrouped them into three basic independent constructs. New research model was designed with three basic independent constructs with three constructs selected for the involuntary information technology usage environment. Finally, this study concluded that new technology acceptance model could be used to explain the use of new technology without any adding new particular independent variables.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.