Overview
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Morgan GERMA: Collaborator at Joseph Fourier University, Grenoble, Master CQAQMV
INTRODUCTION
Often overlooked, error in the specification of a measurement function (often referred to as a "model") can have a significant effect on the results of a measurement method. Modeling, the key step in calibration, is not limited to the point estimation of the parameters of the chosen measurement function (model coefficients). It must also assess the uncertainties surrounding the parameters of this function, in order to estimate the share of uncertainty due to it in the expression of a measurement result. This uncertainty is commonly referred to as "modeling uncertainty". This data sheet covers the mechanisms at work when performing a linear regression, and gives you food for thought when choosing your model.
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Laboratory quality and safety procedures
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Determine a model that fits observed data as closely as possible
Bibliography
P.H. Cornillon, E.M. Lober, Regression – Theory and application , Ed Springer
Collège Français de Métrologie (CFM): "Application of the new VIM 3 calibration concept" and the associated M-CARE processing software, downloadable from the CFM website
Normative reference
Standard NF T 90-210 "Qualité de l'eau – Protocol for the initial evaluation of the performance of a method in a laboratory".
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