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Distinguishing Poisson from Binomial

  • 08-05-2008 8:07pm
    #1
    Registered Users, Registered Users 2 Posts: 2,456 ✭✭✭


    Hey guys, Im a second year computing student and as part of our course for some ungodly reason we have to learn a module known as statistics. In a little over a week i have to sit an exam in this module. It is not my strong module by any means but i am confident i can scrape a pass *(fingers crossed).

    Anyways my main question is we are doing poisson and binomial distribution and apparently in the upcoming test we have to know which one to use. Now from looking over numerous papers i can see no way by which one would be able to distinguish which method to use. I have been told that poisson is random in occurence but biomial can be too so that doesn't give me much of a clue. Any chance anyone here knows how to distinguish between the two?

    All help would be greatly appreciated.Thanks in advance to anyone *(if anyone) answers this. Im in a bit of a pickle!


Comments

  • Registered Users, Registered Users 2 Posts: 16,201 ✭✭✭✭Pherekydes


    In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.


    In probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate and independently of the time since the last event.

    Hope that helps.


  • Closed Accounts Posts: 6,081 ✭✭✭LeixlipRed


    Just seen this now. An example of a Binomial distribution would be flipping a coin. For example what is the probability of getting x number of tails when I flip a coin n times. Here you have a yes/no experiment. Either you flip tails or you flip heads. Nothing else occurs (ignore the unlikely event of the coin landing on its edge or course).

    An example of Poisson would be say, statistical analysis of the cars (remember its discrete) leaving a certain car park has shown that on average 3 leave per hour. Then using this observed figure you can work out the probability of x cars leaving the car park in one hour.

    That's simplifying a lot. But should help you recognise the difference between one question and the other.


  • Registered Users, Registered Users 2 Posts: 2,456 ✭✭✭stick-dan


    thanks guys!


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