Description Usage Arguments Details Examples
bigen
, takes two parameters p and n and repeats an experience with success probability p and repeat it n times. Then returns number of success.
bigen.visual
, takes two parameters p and n like bigen
and plots a Binomial distribution with taking 1000 data from bigen
.
1 2  bigen(p, n)
bigen.visual(p, n)

p 

n 

In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes<e2><80><93>no question, and each with its own booleanvalued outcome: a random variable containing single bit of information: success/yes/true/one (with probability p) or failure/no/false/zero (with probability q = 1 <e2><88><92> p). A single success/failure experiment is also called a Bernoulli trial or Bernoulli experiment and a sequence of outcomes is called a Bernoulli process; for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance.
The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, the draws are not independent and so the resulting distribution is a hypergeometric distribution, not a binomial one. However, for N much larger than n, the binomial distribution remains a good approximation, and is widely used.
1 2  bigen(0.5, 10) ##{1...10}
bigen.visual(0.5, 10)

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