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Missing observations, such as those in the cellCount column of the Milk table, are said to be null.
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However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams.
A previous research by Hanssen et al. (2001) mentioned that, in case of SAR interferometry with higher radar frequency such as X-band and C-band, strong absorption of microwave energy due to severe cloud cover is likely to occur, resulting in missing observation under such a situation.
With such data, removing participants with missing observations from the analysis will lead to a biased sampling of the study population, and for this reason we chose multiple imputation as the approach to handle missing data.
*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
The procedure can also be applied to future power evaluations of other cardiovascular parameters, such as the QT interval, and the loss of statistical power due to missing observations.
Regarding the possible relationship between country controls and missing observations, a logit regression of missing values for our dependent variable on country controls supports the absence of such a relationship.
Third, all relevant variables are screened for missing observations.
The last observation carried forward (LOCF) was used to analyze missing observations.
*Three missing observations.
Some variables have missing observations.
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