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
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
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.