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Related to this, the numbers of samples are very confusing.
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Figure 1 outlines this study and the numbers of samples that contributed to each phase.
Limitations of this study include the numbers of samples in each of the groups; there is no enough data to correlate with previous preterm deliveries, hypertension, BMI, asthma, smoking and socioeconomic status of the women.
In this respect, the number of samples would also be of importance but this variable was not analysed in the present study.
Sample frequency: This defines the number of samples taken per second.
In this study, the number of samples and the detection time are several important factors that affect the efficiency of the algorithm.
In this case, the number of samples (N) should not be too large, and synchronization between the users within a virtual array is crucial.
In this simulation, the number of samples is 1024, which is four times as much as that used in the proposed method.
This increased the number of samples used to compute the variance, which tended to increase our power to make correct decisions.
In this way, the number of samples and sample sizes were identical for all markers.
For this analysis, the number of samples required for reliable statistics increases exponentially with the size of the network.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com