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When the proportion of missing data becomes too large, no statistical technique will provide the solution.
When the amount of missing data becomes large, study results can be inconclusive.
First, as with any longitudinal study, missing data becomes more extensive at each successive stage of data collection.
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For SNPs with GenTrain scores higher than 0.4, the rate of missing data became infinitesimal and the average rate of missing data per successful SNP was very low, with an average of 0.61% for white spruce and 0.40% for black spruce (Table 3), thus less than 1%. 1 Average call rate is 100% minus average number of missing data in %.
To avoid this problem, missing data imputation becomes an important part of analysing economic data.
For real datasets, the choice of methods for dealing with missing data can become a critical component of the data processing.
Response to a survey is more likely when the questionnaire is shorter [ 27] and there may be fewer missing data meaning it becomes feasible to calculate a score as suggested for the SIS [ 28].
However, the missing data on education become smaller during the time period of the study.
When the data are scaled, 0 will become, 254 will become and missing data will be indicated by the value 255.
Equally, in research, missing data from patients who have become too ill to record assessments or who have died become a serious problem[ 2, 3].
Although most data collection was primarily prospectively performed, pathology data were obtained retrospectively once printed specimen pathology reports became available, or missing data were found on later review.
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