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#1 |
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Hypothesis Testing involving Poisson Distribution
Hi, Could anybody help me solving it? I am sure how to do a hypothesis test of Poisson distribution with small sample.
A person will decide to have her telephone disconnected if her average number of calls per day is lsess than 2. On five randomly selected days, her recorded number of calls were 0, 2, 1, 1, 1. a) Briefly state why the Poisson(λ) assumption is suitable as a probability distribution for the number of calls. b) At the 5% level of significance, and based on information from this random sample, discuss whether she should decide to disconnect her phone. Any help would be much appreciated! Thanks! Last edited by marsuconn; 11-06-2009 at 09:51 AM. |
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#2 |
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a) I guess Poisson distribution is suitable for modelling the frequency of rare events
Not quite sure about the discrete test b) Model: Xi ~ Poission(λ), i = 1, 2, 3, 4, 5 Test H0: λ = 2 vs H1: λ < 2 Test Statistics = (X1 + X2 + X3 + X4 + X5) ~ Poission(5λ = 10) under H0 Observed Test Statistics = 5 Pr{(X1 + X2 + X3 + X4 + X5) ≤ 5} ≈ 0.06708596 > 0.05 Therefore, H0 is rejected She should disconnect her phone |
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#3 | ||
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Quote:
http://en.wikipedia.org/wiki/Poisson_distribution Thus perfect for such problems. I wouldn't make any statements about rare events, though the Poisson has a "tail", other distributions would be better suited for 'rare' event modeling e.g. negative binomial. At least in my opinion. Quote:
Is there any particular reason why you rejected H0 here?
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| The Following User Says Thank You to TheEcologist For This Useful Post: | marsuconn (11-08-2009) |
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