The accuracy of the method has been analysed indirectly, by comparison with known modal solutions of rectangular plates, and directly, by assessing the boundary error.
We finally evaluated the performance of our quasi-optimal designs by assessing the extrapolation error over the M T =196 fields in the test set, which were not used for model learning.
This is achieved by assessing the mean squared error between the past and the future reference blocks.
Finally, the potential influence of systolic arterial pressure, pulmonary systolic artery pressure, and hematocrit (all factors known to potentially influence these methods) by assessing the bias and relative error of these three variables.
By assessing the implications of random errors and omissions on population descriptions and mortality estimates using surveillance data collected over a 10-year period in BRHP, this study has addressed important issues regarding the effects of measurement error on DSS study results.
Therefore, we assessed the error by examining the change in hypocenters caused by changes in the velocity structure and by different combinations of stations.
Third, we assessed the error introduced by the method, such as polymerase and reverse transcription error by comparing allelic imbalance between technical replicates (cDNA templates created separately from the same RNA pool).
Hence, it was necessary to assess the errors introduced by these technical factors.
Although the correlation analysis (as seen in Fig. 1) can be helpful in assessing the systematic error (by comparing the correlation slope with the identity line), trueness and precision are inversely related to the sum of the residuals and are not easy to grasp.
