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The calculation of confidence intervals relies on either the statistical assumption of approximate normality for the distribution of the risk indicator, or a suitable mathematical transformation (e.g., logarithmic).
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Koyama and Chen however did not consider such an approach, and their confidence intervals rely on Clopper Pearson method.
Taifour et al. [18] propose the neighborhood back-off algorithm (NBA) where the initial back-off interval relies on the number of neighbor nodes.
The former guarantees always the same quality degree, while the latter is used for dynamic adjustment at runtime: each interval relies on two numerical scalars that are used for specifying the minimum and maximum QoD bounds respectively, and the QoD Engine component adjusts κ within the interval as needed.
Calculation of the 95% confidence interval relies on the SE, which in this case is 2.0/v48 = 0.29.
The calculation of a binomial confidence interval relies on approximating the binomial distribution with a normal distribution.
The existence of this interval relies on the stochastic ordering of the distribution of (M,S) with respect to Π[ 10].
However, due to ethical and practical considerations in children, reference interval determination often relies on large hospital databases, which, by applying appropriate selection criteria, ensure sufficient number of samples [ 33, 34].
The calculation of a 95% confidence interval, as described above, relies on two assumptions: that the distribution of sample means is approximately Normal and that the population SD can be approximated by the sample SD.
No one relies on them".
For the 15 s inter-burst interval we relied on characterizing the endocytic time scale as (1/rate) as the time frame was too compressed to allow accurate exponential fitting.
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