• 국제지역연구
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• 제13권2호
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• pp.89-115
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• 2009

본 연구는 EU 회원국들의 소비위험분산 행위를 살펴보기 위해 기존의 소비위험분산 측정방법에 소위 이례적 관측치 (outlier)를 고려하여 기법을 도입하고 있다. 본 연구는 단순히 소비위험분산을 측정하는 기존의 방법에서 더 나아가 어느 국가 혹은 지역들이 소비위험분산에 특히 더 기여하는지를 밝히고자 한다. 이를 위해 기존 계량경제학의 회귀분석에서 간과되었던 특정 관측치의 회귀분석결과에 대한 영향력 분석을 DFFITS와 DFBETAS 접근법을 사용하였다. 이같은 소비위험분산 측정 결과 EU 지역의 소비위험분산 정도는 서유럽국가와 비교적 최근에 EU에 가입한 동유럽국가로 크게 이분되어 있음을 발견하였다.

• 수산해양기술연구
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• 제60권1호
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.