Binomial distribution: Difference between revisions
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imported>Doug Williamson m (Punctuation added) |
imported>Doug Williamson (Add 'limited' before 'whole number' to differentiate from Poisson distribution.) |
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The binomial distribution can be a useful model for processes where: | The binomial distribution can be a useful model for processes where: | ||
#The process consists of a whole number of identical trials or situations (n). | #The process consists of a limited whole number of identical trials or situations (n). | ||
#Each trial results in just one of only two possible outcomes (eg success or failure). | #Each trial results in just one of only two possible outcomes (eg success or failure). | ||
#The probability of success (p) remains constant for each trial. | #The probability of success (p) remains constant for each trial. | ||
Revision as of 11:03, 7 August 2014
Statistics.
A discrete probability distribution built up from a series of binomial trials.
The binomial distribution can be a useful model for processes where:
- The process consists of a limited whole number of identical trials or situations (n).
- Each trial results in just one of only two possible outcomes (eg success or failure).
- The probability of success (p) remains constant for each trial.
- The trials are independent, and
- Primary interest lies in the probability of a specified number of successes (or of failures) in the n trials.
For example, the total number of sales achieved in a fixed number of sales appointments, assuming the probability of achieving a sale remains constant for each appointment.
