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In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models.
To investigate the effect of varying sample sizes, we created data sets with 64, 128, and 192 taxa by sampling from the 256 sequences generated under the normal distribution (σ = 5) in the previous step.
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This value was known as the normalised moisture content (NMC) of the wood sample, the value is said to be 'normalised' because as a percentage change the effects of varying sample size have been removed.
In addition, the effects
We used simulated cohort data sets to investigate the effect of varying the sample size for subsample and 2-sample IV estimators on power, precision, and bias.
For future research, an adaptive μ could be proposed and investigated keeping in focus the effect of varying the sampling time and the noise.
Thus Fig. 2 shows the power to detect an effect size of 0.5, for varying sample sizes.
First, using the district-level TAS framework, we evaluated the effect of varying the total STH sample size while sampling a constant proportion of children at each cluster surveyed for LF.
Open image in new window Fig. 10 Effect of varying the frequency of sampling and the aspect ratio on the percent sweep enhancement upstream of the obstacle a base case, b optimized (d/X = 2/14).
With a view to investigate the effect of varying the number of reflection coefficients, a sample experiment similar to that considered in the previous subsections is chosen.
However, optimizing the protocol for STH surveillance requires consideration of a number of key components, including the effect of varying the survey platform and population (age group), sampling strategy (number of clusters and sample size), and choice of diagnostic method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com