The phrase "aggregates error" is correct and usable in written English.
It can be used in contexts related to data analysis, statistics, or any field where errors are combined or summed up.
Example: "The final report showed that the aggregates error in the data set was significantly higher than expected, indicating potential issues in data collection."
Alternatives: "cumulative error" or "total error".
So, there is a possibility of aggregate error in estimation.
It seems that previous studies may confirm the productivity paradox due to aggregate error.
Therefore, the aggregate error will be very limited and bias error of parameters obtained by the models is less than the previous studies.
Alternatively, we propose to sort the information symbols according to the aggregate error of the linear estimation: ε jk = ∑ l = j k | s ̂ ZF ( l ) − s ZF ( l ) | 2, (25).
Thus, while the error rate per low-coverage genome is modest, the aggregate error in a multiple alignment of twenty such genomes can be substantial.
The bottom part of figure 4 shows the mean error rates for four types of pairs and the aggregate error rates with varying sizes of test sets.
I certainly agree that verifying my estimates would be a great thing to do (particularly the aggregate error rate, not just the individual factors, as you point out).
For example, averaging equation 3 within each group yields Y i = q i + b i X i where X i and Y i are, respectively, the average exposure and average risk in group i (the aggregate error or residual term can typically be ignored).
Our findings agree with previous reports of lower quality annotation for rhesus and our aggregate error score may be a generally useful measure of overall annotation quality for a given genome (a more direct and granular metric than contig N50 although there is a correlation with contig N50).
We first compare the aggregate errors of the first half and second half parts of the symbols (line 2 in Algorithm 4).
