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For a given sample size and k=500 estimated values we evaluate the average of those estimated vectors and the mean squared errors (MSEs).
The results of a simulation study suggest that the Poisson model provides more conservative (lower) estimates of the confidence for a given sample size and should therefore be preferred.
The simulated annealing algorithm was used to calculate the maximum number of alleles captured for a given sample size and data set.
Third, derivation of incidence by comparing prevalence between time points reduces the precision of the estimate for a given sample size and may result in negative point estimates for groups in whom incidence is low [2], unless a method is used that prohibits such estimates.
The results we have shown here are for a given sample size and range of effect sizes.
This method is based on the power of the Wald test of group effect γ for a given sample size and it is briefly described.
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In this context, for a given sample size, we will be concerned with the maximally robust state-feature pair.
The larger the effect size, the larger the power for a given sample size.
As expected, the error decreases with sample size, although for all effective population sizes, the expected error remains more or less constant above a given sample size.
Supporting Information Table 1 gives sample sizes and summary statistics for the study samples by age group, sex, and population.
For each of the BRFs, the reliability coefficient is calculated for any given sample size.
More suggestions(16)
for a given window size and
for a given grain size and
for a given population size and
for a given capture size and
for a given cluster size and
for a given bin size and
for a given domain size and
for a given block size and
for a given step size and
for a given sample sample and
for a given sample type and
for a given signature size and
for a given thread size and
for a given sample purity and
for a given particle size and
for a given body size and
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com