Binomial distribution, Applied Statistics

Binomial Distribution

Binomial distribution  was discovered by swiss mathematician James  Bernonulli, so this distribution is called as Bernoulli distribution also, this is a discrete frequency distribution.

Assumptions of a binomial Distribution

1.       The random experiment  is performed repeatedly with a fixed  and finite  number of trials. N is denoted by number of trials.

2.      There are two mutually exclusive  possible outcomes on each trial which  are known as success and failure success is  denoted by  whereas failure is denoted  by q, and p+q = 1  or  q=1-p1.

3.      The outcomes  of any given  trial does not affect the outcomes  on subsequent  trials means the trials  are independent.

4.      The probability  of success  and failure (p&q)  remains constant from trial to  trial. If in any distribution the p& q  does not remain constant    that distribution cannot be a binomial distribution, For  example  the probability  of getting  head  must remain  the same in each  toss i.e. ½  similarly  the probability  of drawing  4 balls  from a bag containing  6  red  and 10 white balls  does not  change in successive  draws  with replacement, hence it  will be called  as binomial distribution .But in contrary  to this, if balls  are not replaced  after  each trail then it will not be a binomial  distribution.

5.      If all above  assumptions are satisfied , the probability of exactly  r successes in  n trials is given by

6.      P(n=r)=n crprqn-r

Posted Date: 9/27/2012 7:01:55 AM | Location : United States

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