1993Unpublished venueRequires access

REGRESSION ANALYSIS APPLIED TO MODEL TESTING

Jorder, Hj

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Abstract

A new method of analysing model test data is proposed. The method involves the use of a regression analysis to reduce the errors inherent in model testing, it is further suggested that this regression analysis should be performed on a crossplot of model resistance against model weight at constant speeds. In this way it is possible to express the model resistance as a function of model weight and model speed. With this information at hand the resistance and effective power for any ship side and displacement within the range of the model test may easily be found. The theory is tested out on results from an actual model test.

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What this paper is about

A new method of analysing model test data is proposed. The method involves the use of a regression analysis to reduce the errors inherent in model testing, it is further suggested that this regression analysis should be performed on a crossplot of model resistance against model weight at constant speeds. In this way it is possible to express the model resistance as a function of model weight and model speed. With this information at hand the resistance and effective power for any ship side and displacement within the range of the model test may easily be found. The theory is tested out on results from an actual model test.

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Available abstract

A new method of analysing model test data is proposed. The method involves the use of a regression analysis to reduce the errors inherent in model testing, it is further suggested that this regression analysis should be performed on a crossplot of model resistance against model weight at constant speeds. In this way it is possible to express the model resistance as a function of model weight and model speed. With this information at hand the resistance and effective power for any ship side and displacement within the range of the model test may easily be found. The theory is tested out on results from an actual model test.

Key concepts: Regression analysis, Computer science, Function (biology), Statistics, Constant (computer programming), Range (aeronautics), Factor regression model, Mathematics

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