## 6 Conclusions  
The expanded uncertainty values determined for a monoharmonic signal for subsequent output values of the measurement chain consistently aligned with those obtained experimentally. The typical deviation between the actual and estimated expanded uncertainty fell within the range of \(\pm 5\%\). However, in the case of a polyharmonic signal, the resultant expanded uncertainty values were determined with a larger margin of error. This discrepancy is primarily due to the imprecise determination of the error model parameter of the analyzed measurement chain. These parameters were established in a previous study [19], incorporating several simplifications to ensure that the presented analysis example maintained a minimal level of complexity. Upon analyzing the instances of a polyharmonic signal where there is a notable disparity between the estimated and measured expanded uncertainty values, a specific pattern emerges. Each output quantity of the algorithm corresponds to a particular transmittance, which effectively dampens or enhances error signals with a specific spectrum. Consequently, if there is an inaccurate estimation of the parameter of a particular error signal, especially if this signal emerges as the predominant signal in the algorithm's output, the estimation will be erroneous. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(f_{\mathbf{s},\mathbf{\omega}}\), Hz} & \multicolumn{6}{c}{**Relative Error Value \(\delta\) for Expanded Uncertainty Estimation, \%**} \\  & \(S_{2,0}\) & \(S_{2,1}\) & \(T_{2,0}\) & \(T_{2,1}\) & \(T_{1,0}\) & \(T_{1,1}\) & \(T_{1,2}\) & \(T_{1,3}\) \\ \hline  
5.87\) & \(+2.91\) & \(-15.64\) & \(+6.81\) & \(+8.00\) & \(+7.68\) & \(+7.68\) & \(+7.34\) \\ \hline Mean 1  & 4.58 & 4.60 & 5.41 & 4.26 & 7.02 & 7.82 & 7.83 & 4.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the values of the relative error of estimating the expanded uncertainty values obtained by means of a measurement experiment (the case of a monoharmonic signal). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(f_{\mathbf{s},\mathbf{\omega}}\), Hz} & \multicolumn{6}{c}{**Relative Error Value \(\mathbf{\delta}\) for Expanded Uncertainty Estimation, \%**} \\  & \(S_{2,0}\) & \(S_{2,1}\) & \(T_{2,0}\) & \(T_{2,1}\) & \(T_{1,0}\) & \(T_{1,1}\) & \(T_{1,2}\) & \(T_{1,3}\) \\ \hline
1000 & \(+20.52\) & \(+18.96\) & \(-4.26\) & \(+4.16\) & \(+21.57\) & \(+10.47\) & \(+10.47\) & \(+9.63\) \\
2000 & \(+8.99\) & \(+5.90\) & \(-5.19\) & \(-0.01\) & \(-22.21\) & \(-22.25\) & \(-22.25\) & \(-7.15\) \\
3000 & \(-12.69\) & \(+16.92\) & \(+8.45\) & \(+9.16\) & \(-15.20\) & \(-13. This observation highlights a crucial insight--even with highly accurate estimations of the resultant error signal parameters of the input quantities of the WT algorithm, there remains a possibility that the parameters of the error signal at the algorithm's output are inaccurately estimated. The dominant error signal (with accurately determined parameters) could be suppressed, while the less significant signal (with imprecisely determined parameters) could undergo significant amplification. It can be seen that the transfer function of the WT algorithm can be determined in two ways. The first method, described in [16; 17; 18], is the most accessible to the designer of the measurement track and does not require any knowledge about the algorithm used; however, its use requires a ready implementation of this algorithm. The second method, presented in this paper, requires knowledge of the assumptions of the wavelet family used and their transformation to the form described in Equation (2), by following the steps below:  
1. Indicate the form of the vector of the output quantities based on the number of input quantities, the type of wavelet, and the number of iterations of the signal decomposition process (as shown in Equations (8) and (9) in the case of the analyzed example). 2.