Wikipedia 10K Redux by Reagle from Starling archive. Bugs abound!!!

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BinomialDistribution|Revisited

The ''binomial distribution density function'' provides the probability that X success will occur in N trials of a ''binomial experiment''. 


A ''binomial experiment'' has the following properties.


*1).  It consists of a sequence of N individual trials.
*2). Two outcomes are possible for each trail, success or failure.
*3). The probability of success on any trial, denoted p does not vary from trail to trial.
*4). The trials are independent.

One common example of a binomial experiment is a sequence of N tosses of a coin.
 Here:


*1). This is clearly an experiment that consists of a sequence of N trials.
*2). There are only two outcomes, Heads and Tails, Let call Heads a success and Tails a failure.
*3). The probability of success on any one trail, denoted p, here ½, does not vary from trial to trial.
*4). The trails are independent – The success of the third trial, say, does not depend on the success of the 2nd trial, etc.


To find the binomial distribution function of X successes in N trials, denoted f(X),
we let p denote the probability of success on any one trial. Then the probability of failure on any one trial is q=1-p.  Then the probability of any particular sequence of X success (heads) out of N trials (tosses) is p^X*q^(N-X). But, there are a variety of sequences of tosses that will result in X successes out of N trials. Then, using the formula for the number of ways of obtaining X successes out of N trials, we find that the number of ways of picking X objects out of N objects that there are
*         (N!/(X!*(N-X)!) ways, where '''N'''!=N factorial.

*          We will denote (N!/(X!*(X-N)!) by:


*                                     (N) 
*                                     (X).    (apologies…for symbolism)


Then the probability of N successes out of N trials is given by the FunctioN:


       F(X)=  (N) *  p^X*q^(N-X) = (N) * p^X*(1-p)^(N-X)                                    
              (X)                  (X)


For example, consider the experiment of tossing a die with the usual 6 sides. Suppose we want to know the probability of getting three  "1"s in 5 tosses.
Then p=1/5. So q=4/5.


Then, using the function of the binomial distribution function for 3 successes out of 5 trials we get the probability of this occurring as:

*   F(2)= (5!/(3!*2!)) * (1/5)^3 * (4/5)^2=(5*2)* (1/125)*(16/25)=(10)* (16/3023)          *                         =160/3125=.0512