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Missing records total 8,066, accounting for 2.0%.
I miss record stores.
Never miss recording your reasoning results.
Missing records were only 68 (3.7 %).
Where there were small numbers of missing values, records were deleted (seven records).
After removing records with missing type of care, length of stay and discharge reason, the dataset reduced to 11289 records (we obtain 12537 records by deleting only records with missing type of care, cf. Table 1).
The redundant objectives were deleted while missed ones were added.
Following this extraction process, the remaining duplicate records were deleted, leaving 571 records for analysis.
Crime against criticism: Missing recording.
If any of these variables were missing the record was deleted for the analysis.
After deleting the records of 2632 women who had missing values for race-ethnicity, there were 141,338 records remaining.
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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