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In terms of the concept of independence, the experiment leading to the binomial distribution can be described as follows.
The binomial distribution can be approximated as the Poisson distribution p n, nπ r s 2 / Ω.
For large (which in the biosensor context is certainly the case), this Binomial distribution can be approximated by a Gaussian (2).
Assuming that each pore has the same POH as all the other pores in the same range interval, the binomial distribution can be used as in[28].
The negative binomial distribution can be derived from a two-stage model for the distribution of a discrete variable Y (Venables and Ripley [2002]).
It is well known that for a large number of trials (i.e., when increases) the binomial distribution can be approximated by a normal distribution.
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Odds ratios for receiving a statin and confidence intervals for binomial distributions can be obtained from the online Additional file 1. Subjects were stratified according to primary or secondary prevention and groups were analyzed separately.
The results proved that the beta-binomial distribution can be very useful for analyzing vegetation landscapes.
In a fixed-effects model, the beta-binomial distribution can be used when there is overdispersion with respect to the binomial distribution.
Furthermore, the binomial probability distribution can be approximated with the normal distribution as P N r = x ≅ 1 2 π * V N r exp - x - E N r 2 2 * V N r (13).
The probability mass function (pmf) of a random variable having a ZIP or zero-inflated negative binomial (ZINB) distribution can be written as begin{array}rcl@ Pr(Y_{i}=k)=pi_{i} I k=0)+ 1-pi_{i} g k|{boldsymbol{theta}}_{i}), k=0,1,2,ldots, end{array} (1).
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