BinomialDistribution/Revisited: Difference between revisions

From Wikipedia, the free encyclopedia
Content deleted Content added
No edit summary
 
m Automated conversion
Line 1:
The ''binomial distribution density function'' provides the probability that X success will occur in N trials of a ''binomial experiment''.
 
 
:''See also :'' [[BinomialDistribution]]
 
 
 
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
 
* (X!/(N!*(N-X)!) ways, where '''N'''!=N factorial.
 
 
 
* We will denote (N!/(X!*(N-X)!) 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 binomial probability 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
 
----
 
Your presentation is more complete, certainly, but I fail to see my error. Please look again at BinomialDistribution. Thanks. [[Dick Beldin]]----
 
Please go to BinomialDistribution, where I have indicated what the errors were and how to see them. There errors will remain in "Previous Versions" and stand as evidence for the errors. Are they yours? Yes, I corrected them after over a week. No, there are no major errors anymore. RoseParks
 

Revision as of 00:51, 27 January 2002