Solving Poisson Process Problem: Probability of Mis-Diagnosis

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The discussion centers on a problem involving a Poisson process related to the malfunctioning of an electronic switching device. The device is deemed satisfactory if it produces no more than 1 error during a 5-hour test period, with an average error rate of 0.2 errors per hour. Participants confirm that the problem can be solved using the Poisson distribution, specifically calculating the probability of observing more than 1 error. The mean for the 5-hour period is established as 1, and the probability of misdiagnosis as unsatisfactory is calculated as approximately 0.2642. The conversation highlights the importance of correctly interpreting the problem's wording and applying the appropriate statistical methods.
ankitj
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Hi everyone,

I am stuck on the problem below. I think it has something to do with Poisson Process??
I would really appreciate it if someone could point me in the right direction.

An electronic switching device occasionally malfunctions and may need to be replaced. It is known that the device is satisfactory if it makes, on average no more than .2 errors per hour. A particular five hour period is chosen as a “test” on the device. If no more than 1 error occurs, the device is considered satisfactory. What is the probability that a satisfactory device will be mis- diagnosed as “unsatisfactory” on the basis of the test?

Thanks
AJ
 
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This has to do with the Poisson Distribution. First you need to figure the mean and the variance of the distribution for the 5-hour period, then calculate the "tail" probability beyond 1.
 
Is this correct?

λ = 5*0.2 = 1
P(x>1) = 1-P(x<=1) = 1-(P(x=0) + P(x=1)) = 0.2642

Thanks
AJ
 
The question seems strangely worded. The problem indicates that the machine has an expected error rate of 0.2/hr. Then it asks for the probability that it will be "misdiagnosed" as unsatisfactory. The probability it is misdiagnosed may have nothing to do with whether the machine malfuctions. Bad choice of words in my opinion. I suspect what the question is getting at is if X is a random variable that counts the number of errors in the 5-hour window, then what is the probability that X is more than 1.

This is a Poisson distributed random variable (as has been mentioned). The probability distribution function for a Poisson variable with expected rate per unit interval \lambda is:

P(X=n)=\frac{(\lambda t)^n e^{-\lambda t}}{n!}\text{ for }n=0,1,2,3,\dots

where t is the length of the interval over which X is measured.

Here you have \lambda=0.2\text{ and }t=5 and you're looking for

P(X&gt;1)=1-P(x\leq1)=1-P(X=0)-P(X=1).

You should be able to navigate from here.

--Elucidus
 
Thank you very much for your help. Seems like i was on the right track.
 
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