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brokeninside
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1. In any one-minute interval, the number of requests for a popular Web page is a Poisson random variable with expected value 360 requests.
A Web server has a capacity of C requests per minute. If the number of requests in a one-minute interval is greater than C the server is overloaded. Use the central limit theorem to estimate the smallest value of C for which the probability of overload is less than 0.025.
Because it's a Poisson distribution then E[X] = 360 = alpha = Var[X]
I'm using a Z table, so at 0.5-0.025 = 0.475, Z = 1.96
so Phi((x-360/sqrt(360)) = 1.96
and I get x = 397.1884 which is wrong.
am I on the right track, or completely off?
A Web server has a capacity of C requests per minute. If the number of requests in a one-minute interval is greater than C the server is overloaded. Use the central limit theorem to estimate the smallest value of C for which the probability of overload is less than 0.025.
Because it's a Poisson distribution then E[X] = 360 = alpha = Var[X]
I'm using a Z table, so at 0.5-0.025 = 0.475, Z = 1.96
so Phi((x-360/sqrt(360)) = 1.96
and I get x = 397.1884 which is wrong.
am I on the right track, or completely off?