# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 6 Conclusions  
The experiments conducted demonstrate that the method for determining the resulting expanded uncertainty yields results nearly identical to those obtained through the Monte Carlo method. This method is applicable for the specified confidence level and exhibits minimal sensitivity to its conditions of use, such as the number of error signals or the range of expanded uncertainty values of these signals. In most scenarios, the results obtained using this method prove more accurate than those derived from the approach outlined in [1]. Notably, the method boasts a straightforward application algorithm and low computational complexity. However, it is essential to highlight that when accepting an error in estimating the resultant expanded uncertainty exceeding approximately \(\pm 15\%\), the method delineated in the guide [1] should be preferred. A notable drawback of the method lies in the prerequisite of initially determining the shape coefficient values, necessitating knowledge of all probability density function shapes for the analyzed error signals. Despite this, the process is not intricate and allows for analytical, simulation-based, and measurement-based approaches. The determined coefficients can be stored in a microcontroller's memory for subsequent estimation of coherence coefficient values, considering the current parameters of the analyzed error signals. Challenges arise when dealing with correlated error signals, where it is suggested to analyze correlated signal groups separately to determine the resultant expanded uncertainty value for subsequent consideration in the analysis. The key achievements of the authors of this work include:  
* Proposing a correction described in Equation (12), thanks to which the results obtained are much more accurate compared to those described so far in the literature [18];
* Indication of the difference in the effectiveness of the method for different ways of determining the shape coefficient values, described in Equations (10) and (11);
* Examining the effectiveness of the proposed method depending on the value of the confidence level parameter, number of error signals and their expanded uncertainty range;
* Comparison of the effectiveness of the method to the classical approach proposed in the guide [1]. In conclusion, the employed analysis method is justified when precise determination of the resultant expanded uncertainty is crucial, irrespective of the number of error signals or the chosen confidence level. Its seamless integration within the measurement system's software and minimal computational demands make it a practical choice. Notably, alterations in signal parameters entail no additional calculations, underscoring a key advantage of this method. Future research endeavors could involve refining the method for estimating shape coefficients with greater precision than outlined in this study. Additionally, exploring the analysis of correlated error signals, particularly when the signal count exceeds two, presents a significant area for investigation. One potential approach could involve a strategy akin to that proposed in Equation (14), where a function determining the coherence coefficient value for each pair of distributions individually is derived, or delving into the feasibility of employing artificial neural networks for coherence coefficient estimation. To facilitate user understanding of the method's functionality, an illustrative application example has been developed for the GNU Octave (Authors used GNU Octave version 9.2.0, Copyright (C) 1993-2024 The Octave Project Developers) program [20]. This example enables users to replicate the results demonstrated in the study and gain familiarity with the method's operational algorithm. The example encompasses shape coefficient values for confidence levels \(\gamma\in[50;99]\%\) and \(\gamma\in\mathbb{N}\). Users can access the example on GitHub [21]. Data curation, L.D. ; formal analysis, M.K. and J.R.; funding acquisition, M.K. ; investigation, L.D. ; methodology, M.K. and L.D.