The results show that for the studied chars, ignoring the phenomenon of fragmentation, diffusive and kinetic data are overestimated by about 5 10%, whereas not taking into account the actual shape of the particles overestimates the diffusive data between 45% and 50% and the kinetic data between 25% and 30%.
For example, in a study of cancer incidence in Dalgety Bay, Scotland, risks based on census data were overestimated because there had been rapid population growth in the area since the previous census (Black et al. 1994).
Even so, there is a possibility this proportion was overestimated, as data for one-quarter of the women were missing, possibly indicating lack of work in these women.
In the modified data, the 95%CI was overestimated by 11% by Poisson regression with scale parameter adjusted by χ.
In both data sets, the mean predicted value was overestimated in the lowest quartile group, but underestimated in the highest quartile group.
Previously, the role of HGT in Methanosarcina spp. was overestimated due to biased data.
Otherwise, these strength data would be overestimated by a few tens of percent with a uni-material model of the LED die.
To assess the extent to which incidence rates calculated for common ocular diseases by using claims data may be overestimated according to the length of the disease-free look-back period used in the analysis.
In the case that both effects are in the opposite direction in the data, β1 is overestimated (bias between 20 and 37%), but γ1 is reasonably unbiased (bias between −11 and 22%).
Some of these data may be overestimated: counts of doctors and nurses in public service are derived from the personnel salary administrative system, but the total number registered may include many who are not working at all due to unemployment, illness or other reasons.
