• Journal of Korea Water Resources Association
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• v.37 no.10
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• pp.869-880
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• 2004

The purpose of this study is to examine relationship between water consumption and economic growth in Korea, and to obtain policy implications of the results. To this end, we attempt to provide more careful consideration of the causality issues by applying rigorous techniques of Granger causality. Tests for unit roots, co-integration, and Granger causality based on an error-correction model are presented. The existence of bi-directional causality between water consumption and economic growth in Korea is detected. This finding has various implications for policy analysts and forecasters in Korea. Economic growth requires enormous water consumption, though there are many other factors contributing to economic growth, and water consumption is but one part of it. Thus, this study generates confidence in decisions to invest in the water supply infrastructure. Moreover, this study lends support to the argument that an increase in real income, ceteris paribus, gives rise to water consumption. Economic growth results in a higher proportion of national income spent on water supply services and stimulates further water consumption.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.901-908
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• 2010

A geometrical description method is proposed to represent the process of the forward selection and backward elimination methods among many variable selection methods for multiple regression models. This graphical method shows the process of the forward selection and backward elimination on the first and second quadrants, respectively, of half circle with a unit radius. At each step, the SSR is represented by the norm of vector and the extra SSR or partial determinant coefficient is represented by the angle between two vectors. Some lines are dotted when the partial F test results are statistically significant, so that statistical analysis could be explored. This geometrical description can be obtained the final regression models based on the forward selection and backward elimination methods. And the goodness-of-fit for the model could be explored.

• Journal of The Korean Association For Science Education
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• v.35 no.5
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• pp.919-929
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• 2015

This study aims to develop a modeling-based learning program about geologic structures and to reveal the relationship between the argumentation patterns and levels of students' mental models. Participants included 126 second grade high school students in four sessions of modeling-based learning regarding continental drift, oceanic ridges, transform faults, and characteristics of faults. A modeling-based learning program was implemented in two classes of the experimental group, and teacher-centered traditional classes were carried out for the other students in the comparison group. Science achievement scores and the distribution of students' mental models in experimental and comparison groups were quantitatively compared. The video-taped transcripts of five teams' argumentation were qualitatively analyzed based on the analytic framework developed in the study. The analytic framework for coding students' argumentation in the modeling-based learning was composed of five components of TAP and the corresponding components containing alternative concepts. The results suggest that the frequencies of causal two-dimensional model and cubic model were high in the experimental group, while the frequencies of simple two-dimensional model and simple cross sectional model were high in the comparison group. The higher the frequency of claims, an argumentation pattern was proven successful, and the level of mental model was higher. After the rebuttal was suggested, students observed the model again and claimed again according to new data. Therefore, the model could be confirmed as having a positive impact on students' argumentation process.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.310-310
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• 2019

본 연구에서는 기상청 ASOS(종관기상관측장비) 자료와 통계적 기반의 다중회귀분석모형을 이용하여 경안천 유역에 대한 봄철 강수량(3~5월 누적강수량)의 예측성을 평가하였다. 예측대상기간은 2006~2018년이며 예측인자로서 전국 96개 지점의 ASOS 자료 중 35개 기상요소에 대한 월 자료를 활용하였다. 전망기간(1~12개월)에 따라 강수량 기준 최소 1개월에서 최대 24개월까지의 지체시간을 고려하여 1~24개월 선행 ASOS 기상자료와 강수량 사이의 상관성을 분석하였다. 예측대상년도를 기준으로 과거 40년간의 자료를 이용하여 상관성 분석을 수행하였으며, 상관성이 높은 상위 30개 기상인자를 조합하여 다중회귀분석모형의 예측인자(독립변수)로 활용하였다. 예측대상년도와 전망기간에 따라 최적의 예측인자를 조합하고, 교차검증을 통하여 각각 4,000개의 다중회귀모형을 도출하여 예측범위를 산출하였다. 다중회귀모형에 의한 예측범위를 분석한 결과, 2013년 자료까지는 예측범위가 관측값을 잘 포함하고 예측값의 평균이나 중간값이 관측값과 유사하게 나타난 반면, 2014년부터는 전망기간에 따라 관측값과 예측범위의 차이가 크게 나타나는 경우도 있었다. 예측치의 중간값을 기준으로 3분위(평년 이상, 평년 수준, 평년 이하) 적중률을 분석하면, 2006~2013년에 대해서는 58.3%인 반면, 2014~2018년에 대해서는 11.2% 수준으로 나타났다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1337-1348
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• 2017

In this paper, we develop the high-risk drinking predictive model in Korea using the cross-sectional data from Korea Community Health Survey (2014). We perform the logistic regression analysis, the decision tree analysis, and the neural network analysis using the data mining technique. The results of logistic regression analysis showed that men in their forties had a high risk and the risk of office workers and sales workers were high. Especially, current smokers had higher risk of high-risk drinking. Neural network analysis and logistic regression were the most significant in terms of AUROC (area under a receiver operation characteristic curve) among the three models. The high-risk drinking predictive model developed in this study and the selection method of the high-risk intensive drinking group can be the basis for providing more effective health care services such as hazardous drinking prevention education, and improvement of drinking program.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.47-53
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• 2019

The purpose of the study was to utilize VECM(Vector Error Correction Model) and detect causal relationships among shipping freight rates. Shipping freight rates used in this study were BDI(Baltic Dry Index), HRCI(Howe Robinson Containership Index), WS(World Scale rate) and SCFI(Shanghai Containerized Freight Index). Using weekly data published since August 2nd, 2013 to September 6th, 2019, it was discovered that BDI and WS were heavily influenced by past week's BDI and WS respectively. VECM also found that one percent increase in WS resulted in 0.022% increase in following week's HRCI data. One percent increase in HRCI affects SCFI by 0.77% on the following week. This study believes that finding may help each shipping market of shipping freight rates estimates, thereby encouraging decision markers to exercise discretion and establish best interest decision.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.8
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• pp.845-852
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• 2018

Mayer-Schönberger and Cukier(2013) explain why big data is important for our life, while showing many cases in which analysis of big data has great significance for our life and raising intriguing issues on the analysis of big data. The two authors claim that correlation is in many ways practically far more efficient and versatile in the analysis of big data than causality. Moreover, they claim that causality could be abandoned since analysis and prediction founded on correlation must prevail. I critically examine the two authors' accounts of causality and correlation. First, I criticize that corelation is sufficient for our analysis of data and our prediction founded on the analysis. I point out their misunderstanding of the distinction between correlation and causality. I show that spurious correlation misleads our decision while analyzing Simpson paradox. Second, I criticize not only that causality is more inefficient in the analysis of big data than correlation, but also that there is no mathematical theory for causality. I introduce the mathematical theories of causality founded on structural equation theory, and show that causality has great significance for the analysis of big data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.657-662
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• 2011

The relationship between household attributes and trip generation can only be found in Seoul Metropolitan Household Travel Survey, which has been implemented every 5 years. However, various household attributes' impact on trip generation has not been analyzed closely. This paper compared and analyzed those impact. The results could be useful when trip generation models are studied in the future. They are as follows. The household size should be an important classification criteria when household trip generation is estimated. The traditional assumption that the relationship between household auto ownership and trip generation is positive and linear correlation should be reconsidered. Weekday travel data only did not showed that housing type has an influence on trip generation. Household income is unrelated with trip generation among single-person household, while multi-person household is related strongly. However, when trips are classified by purpose, impact of household income on trip generation are varied by trip purpose. Especially, the increase in single-person household can not be overlooked when trip generation is forecasted.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.1-17
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• 2019

This article analyzes causal relationships among gross domestic product(GDP), electricity consumption, carbon dioxide($CO_2$) emission and foreign direct investments(FDI) inflow of Korea over the period from 1976 to 2014, using unit root test, cointegration test, and vector error correction model(VECM). As the results, this article found (1) a long-run bi-directional causality between GDP and electricity consumption, which may imply a negative impact of electricity consumption-saving policy on economic growth, (2) uni-directional short- and long-run causalities running from $CO_2$ emission to GDP, and a uni-directional long-run causality running from $CO_2$ emission to electricity consumption, which can result in a negative impact of $CO_2$ emission reduction policy on economic growth and electricity consumption, (3) a uni-directional long-run causality running from FDI to GDP, and uni-directional short- and long-run causalities running from FDI to electricity consumption, which may result from relatively lower electricity prices than investing countries, (4) no causality between FDI and $CO_2$ emission, which is based on the characteristics of FDI composed of service industries. Considering the above causal relationships among the four variables, the policy implication needs to focus on the electricity demand management based on the relevant R&Ds, and on the gradual transition from fossil fuel- to renewable-energy. Adaptive policy to increase the FDI inflow is also needed.