n is distributed

binomially with a mean, E(n) = N x PD, and variance of VAR(n) = N x PD(1 - PD).

The statistical significance of differences in the rates of errors detected and recovered was assessed using logistic regression for

binomially distributed data.

After determining if gear saturation occurred,

binomially distributed generalized linear models (GLM's) were fitted to the proportional catch data.

The number of inpatient mental health days was specified as a negative

binomially distributed outcome with a log link; unlike the Poisson distribution, the negative binomial distribution allows for overdispersion in the dependent variable (Hilbe 2007).

We also assumed that the amounts of the fetal-specific alleles for each fetus were

binomially distributed.

2] := m[DELTA] represents a Gilbert-Shannon-Reeds shuffle: cut the deck

binomially with parameter 1/2, then drop the cards one by one from either pile, where the chance of dropping from a pile is proportional to the number of cards currently in the pile.

Therefore, the sum of individual response observations is

binomially distributed with parameters K and [p.

In the previous Nevins and Whitney model (Figure 1), the production process is assumed to be

binomially distributed and the required production yield is set equal to the mean of the binomial.

Given N slots and n tags, r tags in one slot are

binomially distributed with parameters n and 1/N:

x] and are

binomially distributed with parameters ([C.

Logistic regression is a technique that is similar to multiple regression but is used with

binomially distributed dependent variables--in this case, whether the child was declassified.

If the user spreading code is random with odd number of chips N within one data bit duration, then B is

binomially distributed with minimum value zero, average value of (N-1)/2 and maximum value of (N-1) [2,4,8].

Similarly, we posit that among the patients needing each measure's service, the number who receive it are also

binomially distributed, with parameter recvp[i].

The descendent is created by applying variations, called mutations,

binomially distributed (with mean equal to zero and variance [[sigma].

To account for the cross-sectional study design, crude and adjusted prevalence ratios (PR) and 95% confidence intervals (95% CI) were obtained using multivariable regression techniques, specifically PROC GENMOD regression procedures for

binomially distributed variables (SAS Institute, Inc.