Repeat the calculation N times (usually N is 10000 or more).Select, at random, a value from each of the error sources and calculate a value for the measurand in accordance with the defined interrelationships.(Both these steps are the same as the QUAM model.) Decide how they are connected and related.Identify the sources of error and their data shape (distribution).The following principles behind MCS for MU, however, are simple: Monte Carlo simulation modeling In the original QUAM guidance, MCS modeling was discussed briefly and a technical methodology given in Appendix E3 (2), which is quite mathematical intensively and employs Microsoft Excel. This article looks at some examples comparing the results from the traditional QUAM model with Monte Carlo Simulation (MCS) using this tool.
#Mcs calculation tool software
A new cloud-based software tool (Minitab DEVIZE) has become commercially available that makes the process of calculating MU simpler and much quicker (6). The importance of being able to calculate MU easily, therefore, is increasing. Decision rules involving compliance with specification using guard band principles requiring MU have been published as well (4, 5). A new guide was published in 2015 extending MU concepts into decision making and setting target measurement uncertainty (3).
An earlier column showed how the basics of simple error budget calculations based on the Eurachem/CITAC guideline (QUAM) could be performed (1, 2). Performing these calculations need not be so daunting, however. One of the barriers to be overcome in the wider application of measurement uncertainty (MU) to reportable values is the need to perform calculations that can be daunting to many in the analytical laboratory.
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