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Title: Methods For Considering Correlation In Uncertainty Evaluation
Author: Manfred Kochsiek, Bernd Siebert
Source: 2006 Measurement Science Conference
Year Published: 2006
Abstract: Modern uncertainty evaluation is based on both the knowledge about the measuring process and the input quantities contributing to the measurement result 1. Very often, two or more of the input quantities are not independent from each other. The combined uncertainty for the sum or the product of correlated quantities is enhanced and that for the difference or ratio is decreased by such correlation. In everyday practice, however, correlation is often ignored since the relevant uncertainty documents do not provide ready-for-use procedures for identifying and quantifying correlation. The paper provides practical techniques for identifying and quantifying correlation in measurements. Based on a systematic modelling procedure 2, a concept is presented that allows to easily include correlation in the measurement model and to properly estimate correlation coefficients or correlated fractions of the related input quantities either from existing (statistical) data or from other (non-statistical) knowledge 3. Three possible ways to take correlation into consideration when evaluating measurement uncertainty are described and discussed: Resolving correlation by introducing known dependencies on another (third) quantity, the classical way of Gaussian uncertainty propagation and introducing so-called auxiliary quantities representing the correlated fraction of two or more quantities.

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