Barker, R M; Cox, M G; Forbes, A B; Harris, P M (2007) Software Support for Metrology Best Practice Guide No. 4. Discrete modelling and experimental data analysis. NPL Report. DEM-ES 018

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Abstract

Metrology, the science of measurement, involves the determination of physical quantities from experiment, along with estimates of their associated uncertainties. In this endeavour, a mathematical model of the measurement system is required in order to extract information from the experimental data. This involves model building: developing a mathematical model of the experimental system in terms of equations involving parameters that describe all the relevant aspects of the system, and model solving: determining estimates of the model parameters from the measured data by solving the equations constructed as part of the model. This Best Practice Guide for discrete modelling covers all the main stages in experimental data analysis: construction of candidate models, model parametrization, error structure in the data, uncertainty of measurements, choice of parameter estimation algorithms and their implementation in software, with the concepts illustrated by case studies. A www version of the Guide will allow for further sections on models, algorithms and case studies to be added.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	DEM-ES 018
Keywords:	data modelling analysis
Subjects:	Mathematics and Scientific Computing Mathematics and Scientific Computing > Modelling
Last Modified:	02 Feb 2018 13:15
URI:	http://eprintspublications.npl.co.uk/id/eprint/4606

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