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By Franco (EDT)/ Forbes, Alistair B. (EDT) Pavese

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Additional resources for Advances in Data Modeling for Measurements in the Metrology and Testing Fields

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As a matter of fact, in most cases, the data supplied by each laboratory to its users as a calibration, or to a comparison in the case of inter comparisons, should be considered representing the typical capability of the laboratory, that is, as samples from the population of repeated measurements performed on the specific laboratory standard. , local) population; it is not a summary statistic of only the (few) specific measurement results obtained for the calibration, or for the comparison. It is assumed, instead, to be consistent with the expectation of the local standard, as currently maintained.

An Introduction to Data Modelling Principles in Metrology and Testing 19 – The first pair of subsequent terms are estimates performed within each laboratory: ε is the component of the total error that is assumed to be common to all laboratories, therefore not depending on the subscript i; εi is the zero-mean and with variance depending on the laboratory; – The second pair of terms, “(possibly zero) arising from unforeseen or incompletely assessed sources of error”, additionally holds specifically in the case of comparisons: ε is “the shared offset of which the laboratories are unaware, be it a reflection of the state of the art, an offset due to incomplete understanding of the measurement model or .

Using effective degrees of freedom Metrologia 40 31–35 Chunovkina A and Cox M G 2003 A model–based approach to key comparison data evaluation, in Proceedings 17th IMEKO World Congress, Cavtat, Slovenia, on CD–ROM Chunovkina A, Ciarlini P, Cox M, and Pavese F 2004 Handling and treatment of measurement data from different groups or sources, in Proceedings 10th IMEKO TC7 International Symposium, on CD–ROM Ciarlini P, Cox M G, Pavese F, and Regoliosi G 2004 The use of a mixture of probability distributions in temperature interlaboratory comparisons Metrologia 41 116–121 Ciarlini P, Chunovkina A, Pavese F, and Regoliosi G 2005 Mixture distribution models in temperature metrology and the assessment of the degree of equivalence, in Proceedings TEMPMEKO 2004, LPM, Zabreb, Croatia, pp 1003–1008 Cox M G, Forbes A B, and Harris P 2007 Distributions associated with measurands, in PTB–BIPM Workshop on the Impact of Information Technology in Metrology, Berlin, Germany.

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