This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.
Every year governments spend their national budget on public health in order to reduce financial burden of individuals on health. Although it has been widely believed that the increase of public expenditure on health decreases private health expenditure, it has not been proved by analysis with real data. For better understanding, we conducted an empirical study on the real data of 17 OECD countries-Australia, Austria, Canada, Denmark, Finland, Germany, Iceland, Ireland, Japan, Korea, New Zealand, Norway, Portugal, Spain, Sweden, the United Kingdom, and the United States. The panel Granger-causality test is used to verify the cause-and-effect relationship between the two expenditures. As a result, public expenditure on health has a 3 to 4 year-lagged negative effect on private health expenditure in the cases of the 16 countries except for the United States.
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