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Dichotomous outcomes were modeled for each form of bullying using a binominal distribution and a logic link function.
For both cases, the probability of a random occurrence was estimated using a binominal distribution: the probability of finding 2 columns of group 1 in a window of 9 columns is 2%, and the probability of finding three or more columns of either group 1 or group 2 is 2%.
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In this article, we proposed a novel backoff algorithm that uses a binominal distribution, instead of a uniform distribution, to select the backoff value.
In this article, we present a novel backoff algorithm that uses a binominal distribution rather than a uniform distribution to determine the backoff value.
Hence, in this article, we present a novel backoff algorithm that uses a binominal distribution rather than a uniform distribution to minimize the degrading effect of random number generators.
Detection performance (percentage correct) was tested for significance for each individual participant using a binominal test evaluated at an alpha level of 0.05.
The relevance of the inquired statements was tested using a binominal test for null hypothesis proportion p0=0.5.
We conducted a test for non-response bias in both settings using a binominal test of proportions and a 5% significance level.
The estimated absolute difference of 6.8%9595% CI: -2.5% to 16.0%) was not statistically significant, P = 0.15 using a binominal generalised linear model.
Briefly, p-values were computed to reflect the significance of the difference between two counts (n1 and n2 corresponding to any two library combination out of the six libraries) using a binominal model.
We next employ a binominal distribution to evaluate the significance of observing such soft-clipped reads caused by the presence of indels.
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