Poisson Probability Distribution

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SUMMARY

The discussion focuses on the Poisson Probability Distribution, specifically analyzing CPU failures in a sample of 10,000 computers with a failure rate of 0.10%. The expected value (E(X)) and variance (V(X)) are both calculated as 10, leading to a standard deviation of approximately 3.16. The probability of more than 10 failures is computed using the cumulative distribution function, while the probability of no failures is derived from the formula p(0;10) = e-10.

PREREQUISITES
  • Understanding of Poisson distribution and its properties
  • Familiarity with the formula p(x;λ) = (eλx)/x!
  • Basic knowledge of expected value and standard deviation calculations
  • Ability to perform summation of probabilities for discrete distributions
NEXT STEPS
  • Study the derivation and applications of the Poisson distribution in real-world scenarios
  • Learn how to calculate cumulative probabilities for Poisson distributions
  • Explore the relationship between Poisson and normal distributions for large samples
  • Investigate the use of statistical software for Poisson probability calculations
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Students in statistics, data analysts, and professionals in quality control or reliability engineering who are interested in understanding failure rates and probability distributions.

exitwound
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Homework Statement



Suppose that .10% of all computers of a certain type experience CPU failure during the warranty period. Consider a sample of 10,000 computers.

a.)What are the expected value and standard deviation of the number of computers in the sample that have the defect?
b.) What is the (approximate) probability that more than 10 sampled computers have the defect?
c.) What is the (approximate) probability that no sampled computers have the defect?

Homework Equations



p(x;\lambda )=\frac{e^{-\lambda}\lambda^x}{x!}

The Attempt at a Solution



a.) E(X) of a poisson distribution is \lambda which is np which is (10,000)(.001)=10.

V(X) is also \lambda or 10.

The standard deviation is the squareroot of V(X) or sqrt(10)?

b.) To do this, would I take the sum of the probability of 10 machines having a failure to infinity cpus failing? (i.e. p(10;10) + p(11;10) + ... p(inf,10))

p(x\geq 10;10)=\sum_{x=10}^{\infty} {\frac{e^{-10}10^x}{x!}}

c.) To find this, would I use p(0;10)? If so:

p(0;10)={\frac{e^{-10}10^0}{0!}}

p(0;10)={\frac{e^{-10}(1)}{1}}=e^{-10}
 
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Looks fine except in the second part, more than 10 probably means strict inequality and since poisson is discrete, you probably need to index the sum starting at x = 11.
 
Ah yes. Thank you :)
 

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