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Prior to performing our statistical analyses, item-level missing data was filled in with imputed values using multiple linear regressions on the cases without missing data.
Both studies [11, 12], however, had methodological limitations; headache diagnosis was not confirmed by a headache specialist, and most of the mental health clinical data was filled out by patients.
For each patient, an information sheet with epidemiologic, clinical, echocardiographic, and biologic data was filled out.
A registration form, including a range of personal data, was filled in by the care provider.
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Any resulting gaps in the data were filled using linear interpolation.
The missing values of the original data were filled by half of the minimum value via a numerical simulation method.
Once your data is filled in, look at the number of times "me" appears in the center column.
A map of the Atlantic in 1900, based on that data, is filled with colored splotches showing concentrations of fish.
Missing data were filled in with data from the POWER database (http://power.larc.nasa.gov).
From here, missing data is filled in using the predicted values of the TALIS missing data.
But the NAHB data are filled with laggards, signifying some realistic thinking.
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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