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The New York Times Natee Silver built his reputation on this -- using a computer model based on polling averages to avoid getting hung up on any individual result.
We first generated weekly averages and then overall exposure averages, to avoid this problem.
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Each algorithm is executed 100 times, and the error is averaged to avoid numerical instability.
(T1 choices were relatively rare at negative coherences, and we therefore omitted negative coherences from the average to avoid measurement noise resulting from small numbers of trials).
Values were subsequently averaged to avoid potential laboratory error associated with single measurements.
Data from patients who experienced multiple exacerbations were averaged to avoid bias through repeated measures.
We therefore decided to use a 1-week average to avoid missing the potential effects of longer lags.
Because the measures from the two Bronx locations were highly correlated across all hours, their daily values were averaged to avoid over-weighting ozone measurements at this location.
When more than one poll was conducted in a year, results were averaged to avoid possible biases deriving from different survey designs.
For the variability measures, the standard deviations for left and right sides were calculated separately and then averaged to avoid the effect of asymmetry on the values.
We subtracted the long-term average from the short-term average to avoid collinearity issues and to ensure that differences between ZIP codes in PM2.5 at a given time did not contribute to the short-term effect estimate.
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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