Article
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Preserved in Portico This version is not peer-reviewed
Risk Assessment for Linear Regression Models in Metrology
Version 1
: Received: 17 February 2024 / Approved: 18 February 2024 / Online: 19 February 2024 (14:52:11 CET)
A peer-reviewed article of this Preprint also exists.
Božić, D.; Runje, B.; Razumić, A. Risk Assessment for Linear Regression Models in Metrology. Appl. Sci. 2024, 14, 2605. Božić, D.; Runje, B.; Razumić, A. Risk Assessment for Linear Regression Models in Metrology. Appl. Sci. 2024, 14, 2605.
Abstract
The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.
Keywords
regression; consumer’s risk; producer’s risk; tolerance interval; measurement uncertainty
Subject
Engineering, Safety, Risk, Reliability and Quality
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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