Binomial distribution: Difference between revisions

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The binomial distribution can be a useful model for processes where:
The binomial distribution can be an appropriate 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.
#The trials are independent and
#The trials are independent, and
#Primary interest lies in the probability of a specified number of successes (or of failures) in the n trials.
#Primary interest lies in the probability of a specified number of successes (or of failures) in the n trials.


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== See also ==
== See also ==
* [[Binary]]
* [[Binomial]]
* [[Binomial]]
* [[Discrete random variable]]
* [[Discrete random variable]]
* [[Distribution]]
* [[Frequency distribution]]
* [[Frequency distribution]]
* [[Poisson distribution]]
* [[Poisson distribution]]
[[Category:The_business_context]]

Latest revision as of 00:29, 27 November 2020

Statistics.

A discrete probability distribution built up from a series of binomial trials.


The binomial distribution can be an appropriate model for processes where:

  1. The process consists of a limited whole number of identical trials or situations (n).
  2. Each trial results in just one of only two possible outcomes (eg success or failure).
  3. The probability of success (p) remains constant for each trial.
  4. The trials are independent, and
  5. 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.


See also