Difference between revisions of "Binomial distribution"
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| − | + | The binomial distribution is used where there are two outcomes and the probability of an outcome remains constant regardless of the # times the experiment is repeated ([[Independent events|Independent events]]: | |
| + | <math>P(r) = {n \choose r} p^r (1-p)^{n-r}</math> | ||
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| + | An elementary example is this: roll a standard die ten times and count the number of sixes. The distribution of this random number is a binomial distribution with n = 10 and p = 1/6. As another example, flip a coin three times and count the number of heads. The distribution of this random number is a binomial distribution with n = 3 and p = 1/2. | ||
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| + | Try out a bit with /static/files/MBI/Module%207/binomial.xlsx | ||
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| + | Part of [[M7-Quantitative Decision Methods-S2 - Probability distributions|M7-Quantitative Decision Methods-S2 - Probability distributions]] | ||
| + | See also:http://en.wikipedia.org/wiki/Binomial_distribution | ||
[[Category:mbi]] | [[Category:mbi]] | ||
Latest revision as of 19:53, 27 August 2020
The binomial distribution is used where there are two outcomes and the probability of an outcome remains constant regardless of the # times the experiment is repeated (Independent events:
An elementary example is this: roll a standard die ten times and count the number of sixes. The distribution of this random number is a binomial distribution with n = 10 and p = 1/6. As another example, flip a coin three times and count the number of heads. The distribution of this random number is a binomial distribution with n = 3 and p = 1/2.
Try out a bit with /static/files/MBI/Module%207/binomial.xlsx
Part of M7-Quantitative Decision Methods-S2 - Probability distributions See also:http://en.wikipedia.org/wiki/Binomial_distribution
