Your English writing platform
Discover LudwigExact(26)
Margins of error varied from 2.6percentt (Australia) to 4.38percentt (Israel).
The margins of error varied, from as low as plus or minus two percentage points to as much as plus or minus four percentage points.
Identification error varied with taxonomic level and lineage, but remained low at the morphospecies level.
The mean relative error varied from 2·0 to 8·3% for six predictions with an average of 4·4%.
During the study period, the average size (±standard error) varied between 32 (±2.4) in Andremba and 40 (±3.0) in Miarintsoa for monitored cattle herds and between 21 (±3.3) in Ankilibory and 43 (±3.2) in Miarintsoa for goats.
The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.
Similar(34)
The measurement error varies obviously during the transient measurement process.
Over the range of time-step size investigated, the dominant time-stepping error varies as Δt3/2.
We can find that the error varies for different update delays and different speeds.
b F. oxysporum; n = 3 and standard error varies from 0.004 to 0.04.
Note in this table that the annual average of the error varies between −7.807 and −9.691%.
Write better and faster with AI suggestions while staying true to your unique style.
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