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These statistics are comparable with English population estimates., The 'decision to participate in PA' had 10 missing observations, whereas 'meeting the recommended level given participation' had none.
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*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
The effects of missing observations on complete and partial diallel cross designs are examined.
Subset designs are studied for their robustness to missing observations in different experimental regions.
*Between 0 3 missing observations per period.
Missing observations in the outcome variable were excluded from analysis.
Missing observations between years were substituted using the last observation carried forward (LOCF) technique.
Third, all relevant variables are screened for missing observations.
The last observation carried forward (LOCF) was used to analyze missing observations.
We propose a new and computationally efficient forward backward algorithm for HSMM with missing observations and multiple observation sequences.
Were all observations included, there would be 684 observations, but because of missing observations, there are 507 observations in the data set.
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