• International Area Studies Review
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• v.13 no.2
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• pp.89-115
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• 2009

By measuring the consumption risk sharing for the EU regions, we evaluate the performance of various risk sharing channels for the EU. We identify which countries are likely to form the highest risk sharing group among the EU regions by using the DFFITS and DFBETAS diagnostics derived in a statistical regression. Our finding suggests that most western European countries seem to display homogeneous degree of risk sharing. In addition, our result confirms that high risk sharing regions as well as low risk sharing regions are mainly located in many eastern European countries that joined the EU later than western European countries, and implies that the EU members are still dichotomized at large in terms of consumption risk sharing.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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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.