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It yielded temperatures with a total error of about 4%.
The uncalibrated two parameters filter generates instead large relative error of about 35%.
The improved PMRS method shows good performance on the remote sensing of hourly PM2.5 with an average error of about 38 μg/m3 and relative error of about 31%.
Previously published experimental data were utilized for the validation process where a maximum error of about 5.8% achieved.
That's a lot of simulations, but it still leaves a small margin of error of about 0.5 percentage points on the daily win probability estimates.
Moreover, the predicted fatigue life in terms of number of startups shows excellent agreement with a maximum error of about 4.2% in the predicted life.
It was observed that the predicted and experimental IFT values at the optimum condition are in good agreement with an error of about 1.5%.
Examinations with geomagnetic field models, however, show that an altitude error of 100 m results in a measurement error of about 2.5 nT whereas a horizontal error of the same size results in a measurement error of about 0.5 nT.
Specifically, enlargement of the dataset to 200 patients (instead of 94) will give a classification error of about 0.05 and a regression error of about 0.2 (Additional file 8).
An error of about 0.8104% is found.
The poll had a margin of error of about four percentage points.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com