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The binomial distribution is one in which you have repeated trials of an experiment in which the outcomes of the experiment are independent, the probability of the outcome is constant.
If there are n trials and the probability of "success" in each trail is p, then the probability of exactly r successes is (nCr)*p^r*(1-p)^(n-r) :
where nCr = n!/[r!*(n-r)!]
and n! = n*(n-1)*...*3*2*1
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How can you approximate a binomial distribution to a poison distribution when the number of binomial trials became large enough?
The Poisson distribution with parameter np will be a good approximation for the binomial distribution with parameters n and p when n is large and p is small. For more details See related link below
What is the single parameter in the binomial distribution and what sample statistic would be used to estimate it?
The binomial distribution is defined by two parameters so there is not THE SINGLE parameter.
Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?
Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?
How many experimental outcomes are possible for the binomial and the Poisson distributions?
The binomial distribution is a discrete probability distribution. The number of possible outcomes depends on the number of possible successes in a given trial. For the Poisson distribution there are Infinitely many.
Is in binomial distribution mean always greater then variance?
yes
