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Exact(27)
Zero missing values occurred within the data.
Most (5/10) missing values occurred during the occlusion phase of the experiment.
Most missing values occurred at three and seven days after treatment (respectively 16 and 12 missing), as patients were at that moment often not able to visit the hospital due to complaints of fever and fatigue, as part of the postembolization syndrome.
Third, the vast majority of missing values occurred because the obvious clues were actually absent.
We assumed that missing values occurred at random given the other covariates and used multiple imputation to derive predictions for missing values.[22] The extent of completeness of data for various predictor variables is represented in Table 1.
If the missing values occurred because, for example, accident victims and eyewitnesses are more likely to report the presence than the absence of obvious clues, then the deletion of 82% of the accidents would shift the distribution of the obvious clues towards more clues and result in inflated prevention values.
Similar(33)
Missing values, occur when no data value is stored for the variable in an observation.
Missing values occur frequently in many different statistical applications and need to be dealt with carefully, especially when the data are collected spatio-temporally.
In principle, the same problem applies when missing values occur in ordinary least squares regression models (e.g., Little [1992]): The corresponding procedures require that all variables be fully observed.
When missing values occur because the records do not contain the sought-after information, researchers should first attempt to establish the meaning of missing values from external data whenever possible (e.g., from historical weather data, see Uttl et al. [15]).
Pairwise available data were used when missing values occur.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com