Sentence examples for binomial test to estimate from inspiring English sources

Under this assumption, we used a binomial test to estimate the probability of observing the proportion of A-to-G mismatches in the A-site footprint dataset at each adenine residue sufficiently covered by reads (217 positions, representing roughly 50% of A positions in all readthrough events reported).

The beta-binomial model was tested to estimate the summary event rate of chronic diseases, which takes into account the variation in the chance of occurrence of chronic NCDs across households and the binomial distribution followed by chronic diseases in each household (Table 2) (39).

We used an exact binomial test to examine the significance of overlap between LRES or LREA regions and earlier or later domains.

This study introduces a formal statistical test known as the "C test" to determine when there is a need to apply the beta-binomial test instead of the binomial test to screen a roadway network.

Binomial test was used to estimate association between each COG category and evolutionary forces (i.e. positive selection and/or homologous recombination); Bonferroni corrections for multiple comparisons were performed according to the number of one-sided tests.

To identify REBs from ordered gene expression data, rather then a use an averaging function to evaluate each window span, an approximated binomial test is used to estimate of the probability, in terms of a z-score, that a gene expression bias exists within each span (see Materials and Methods).

DESeq assumes a negative binomial distribution to estimate variance and mean for each group, and performs statistical test based on it.

We then built the negative binomial model to estimate influenza associated hospitalization rates of each age-disease category.

Finally, we used a negative binomial distribution to estimate the statistical significance of the peaks.

We used logistic binomial regression to estimate relative risks (RR) and control potential confounders.

The binomial test (which estimates the probability of obtaining the observed number of significant tests at the 0.05 level given the total number of tests) was used to detect significant departures from null hypothesis across multiple tests, such between pairwise population comparisons across genes.