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Figure 4 Mean of errors at track end as the function of initial heading error.
Accuracy: (a) Error calculation: calculates the arithmetic mean of errors in each comparison.
Error calculation: calculates the arithmetic mean of errors in each comparison.
Figure 5 Mean of errors at track end as the function of heading change error.
Figure 3 Mean of errors at track end as the function of initial standard deviation of heading.
We also point out that there are differences in the error reporting, mainly in the way how the mean of errors is calculated.
Similar(52)
The fundamental of algorithm is the mean of error upper bound.
The mean of error terms was set to zero.
Mean of error variable is 0. Standard deviation of error variable is a constant.
The mean number of errors at each trial was noted.
(A ) Mean number of errors (±SEM).
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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