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poisson distribution
a discrete probability distribution that applies to occurrences of some event over a specified interval. the interval can be time, space, distance, area, volume, or some similar unit. the random variable, X, is the number of “successes”
what are “successes” in a poisson distribution
the number of occurrences of the event in the specified interval
what makes the poisson distribution different from the binomial distribution
the binomial has a fixed number of trials and therefore, a limit to the number of times the outcome we want can happen. the poisson has a fixed interval of time or space in which the number of “successes” can be recorded, but there is no limit to the number of “successes”
example of poisson vs binomial
binomial: n=4, then at most x=4
poisson: fixed interval is 1 day and there is no limit to the number of times the outcome of the interval can happen in that day
assumptions of a poisson distribution
1) events must occur one at a time, and occurrences of the events are independent
2) theoretically, an infinite number of occurrences of the event must be possible in the interval
3) the probability of a single occurrence of the event in a given interval is proportional to the length of the interval

what is this formula
poisson probability formula
what is mu in the poisson probability formula
the average number of occurrences of the random event in the specified interval
what is e in the poisson probability formula
~ 2.71828
if the poisson distribution question says “at least” why does that change things
because we have no idea what the upper bound would be
what to do if the poisson distribution question says “at least,” with at least 3 being the example
P(x greater than or equal to 3) + P(x less than or equal to 3) = 1
this is complementary so it can also be:
P(x greater than or equal to 3) = 1-P(x less than 3)
when there is “at least” in the poisson distribution question, what does the complementary equation used allow for
the solving of x