Poisson Probability Distribution

In summary, we consider a sample of 10,000 computers with a 0.10% chance of experiencing CPU failure during the warranty period. The expected value and standard deviation of the number of computers in the sample with the defect are both 10. The approximate probability of more than 10 sampled computers having the defect can be calculated using the sum of the probability of 10 or more failures in a Poisson distribution. The approximate probability of no sampled computers having the defect can be calculated using the Poisson formula with a lambda of 10.
  • #1
exitwound
292
1

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



[tex]p(x;\lambda )=\frac{e^{-\lambda}\lambda^x}{x!}[/tex]

The Attempt at a Solution



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

V(X) is also [itex]\lambda[/itex] 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))

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

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

[tex]p(0;10)={\frac{e^{-10}10^0}{0!}}[/tex]

[tex]p(0;10)={\frac{e^{-10}(1)}{1}}=e^{-10}[/tex]
 
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  • #2
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.
 
  • #3
Ah yes. Thank you :)
 

What is Poisson Probability Distribution?

Poisson Probability Distribution is a mathematical concept used to model the probability of a certain number of events occurring within a specific time or space, given a known average rate of occurrence.

What are the characteristics of Poisson Probability Distribution?

There are three main characteristics of Poisson Probability Distribution:

  • The number of events must be discrete (countable)
  • The probability of an event occurring is constant over time or space
  • The events must be independent of each other

How is Poisson Probability Distribution different from other probability distributions?

Poisson Probability Distribution differs from other distributions in that it is used specifically for counting events that occur within a certain time or space, whereas other distributions may be used for continuous or non-countable events.

What is the formula for calculating Poisson Probability Distribution?

The formula for calculating Poisson Probability Distribution is: P(x) = (e^-λ * λ^x) / x!, where λ is the average rate of occurrence and x is the number of events that occur.

What are some real-world applications of Poisson Probability Distribution?

Poisson Probability Distribution can be applied in various fields such as insurance, finance, and biology. For example, it can be used to model the number of accidents in a certain time period for insurance purposes, the number of customer arrivals per hour in a bank, or the number of mutations in a DNA sequence.

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