Sunday, 7 January 2018

Binomial distribution with example

What is Binomial Distribution

    Binomial Distribution is also called as Bernoulli's Distribution. Let X be Binomially Distributed with the parameter 'n' and 'p'.



P(X) = nCx  pxqn-x






In chance idea and statistics, the binomial distribution with parameters n and p is the discrete chance distribution of the variety of successes in a sequence of n independent experiments.

Every asking a sure–no question, and each with its very own boolean-valued final results: a random variable containing single bit of records: achievement/sure/actual/one (with opportunity p) or failure/no/false/zero (with probability q = 1 − p).

A unmarried achievement/failure experiment is also referred to as a Bernoulli trial or Bernoulli experiment and a series of results is known as a Bernoulli process, for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution.

The binomial distribution is the premise for the popular binomial check of statistical significance.

The binomial distribution is regularly used to version the range of successes in a pattern of length n drawn with substitute from a populace of length n

If the sampling is performed without substitute, the draws aren't independent and so the ensuing distribution is a hyper-geometric distribution, no longer a binomial one. But, for n a great deal larger than n, the binomial distribution remains a good approximation, and is widely used.






Note:

  1. Mean = E(X) = np
  2. Variance =  σ2   = npq

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