In this paper we investigate the question of how much combined measurements can increase the accuracy of additive quantities. Therefore, we consider a set of measurements from a selection of all possible combinations of the n labeled masses and then estimate the individual weights of the n masses by a linear regression approach.We present experimental results which motivate comprehensive simulation campaigns. These simulations provide valid statistical statements and reliable forecasts of the experimental results. A profound analytical treatment in turn supports these simulation outcomes with excellent consistency. One important achievement therein is a general analytical expression for the estimate's error, not only limited to the two particular weighing schemes presented.It turns out that combined measurements allow to estimate the weight of mass elements with an accuracy that under-runs by orders of magnitude the resolution of the scale used. As the error depends on the amount of measurements, one gains higher accuracy with increasing effort.In a broader sense, our work wants to promote the method and give inspirations to applications in various metrological fields beyond high-precision mass determination. Moreover, the novel simulations and analytic formulas enable the design of optimal experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.