Sentence examples for binomial distribution was applied from inspiring English sources

The random process was repeated 1000 times and a binomial distribution was applied using the expectation derived from the overlap of CNEs with random regions.

To test the null hypothesis that the number of CNEs that overlap experimentally validated TF binding sites is no greater than that expected from CNEs that overlap randomized TF binding sites, a binomial distribution was applied in the following way.

Comparing estimates based upon these subsets, 11 (7.86%) of the 140 t-tests showed significant differences in the estimates generated, which was non-significant when a 95% confidence interval from a Binomial distribution was applied.

Only six (5.13%) of the t-tests showed significant differences in the estimates generated from each subset of items, which was non-significant when a 95% confidence interval from a Binomial distribution was applied.

Comparison of the person estimates generated from these two subsets indicated that eight (7.41%) of the 108 t-tests showed significant differences in the estimates generated, which was non-significant when a 95% confidence interval from a Binomial distribution was applied.

The same data collection, data selection, and model analysis using negative binomial distribution were applied independently in the 4 data sets to identify 'reproducible' factors, i.e. explanatory factors that were significant in different time periods and spaces.

It was found that 23 (6.39%) of the 360 t-tests showed significant differences in the estimates generated, which is non-significant when 95% confidence intervals from a Binomial distribution are applied to this proportion.

As the measurements 1, 2, 3, and 5 represent binary data, statistical analyses based on generalised linear models assuming a binomial distribution were applied (Table 2), using logit as the link function, and tested with a Chi-square test.

Pearson's χ2 test on a binomial distribution was also applied, and in all cases resulted in p-values between 10-15 and 10.

Binomial distribution was used to calculate 95% confidence intervals (CI), and Fisher's Exact test applied to 2 × 2 contingency tables where necessary.

A Bernoulli (binomial) distribution was used for presence/absence models and a Poisson distribution for count models.