Poisson Probability: At Least 50% Defective Brake Lights

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SUMMARY

The discussion centers on determining the sample size (n) needed to ensure at least a 50% probability of finding at least one defective brake light in a sample of cars, given that 1% of cars have defective brake lights. The problem utilizes a Poisson approximation, where the random variable X follows a Binomial distribution, X-Bin(n, 0.01), which can be approximated as X≈Po(0.01n) for large n. The key equation derived is P(X≥1)≥0.5, leading to the condition ne^-0.01n ≥ 50, which must be solved to find the appropriate sample size.

PREREQUISITES
  • Understanding of Poisson distribution and its approximation to Binomial distribution
  • Basic knowledge of probability theory and statistical concepts
  • Familiarity with exponential functions and their properties
  • Ability to solve inequalities and equations involving exponential terms
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  • Study the derivation of the Poisson approximation for Binomial distributions
  • Learn how to solve inequalities involving exponential functions
  • Explore practical applications of Poisson distribution in quality control
  • Investigate statistical software tools for performing Poisson calculations
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Students in statistics, data analysts, and quality control engineers who need to understand sampling methods and probability calculations related to defect detection in manufacturing processes.

Amannequin
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Homework Statement



Suppose that 1% of cars have defective brake lights and n cars are to be inspected. How large should n be for the sample to have a probability of at least 50% of containing a car with a defective brake light? Give an answer using a Poisson approximation with an appropriate mean.

The Attempt at a Solution



Let X-Bin(n, 0.01).
We can approximate X with the Poisson distribution assuming n large and with mean 0.01n.
That is, X≈Po(0.01n).
We want P(X=1)≥ 0.5 which yields ne^-0.01n ≥ 50.

Then I'm stuck. Is this correct so far and any direction on where to go from here will be appreciated. Thanks.
 
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Amannequin said:

Homework Statement



Suppose that 1% of cars have defective brake lights and n cars are to be inspected. How large should n be for the sample to have a probability of at least 50% of containing a car with a defective brake light? Give an answer using a Poisson approximation with an appropriate mean.

The Attempt at a Solution



Let X-Bin(n, 0.01).
We can approximate X with the Poisson distribution assuming n large and with mean 0.01n.
That is, X≈Po(0.01n).
We want P(X=1)≥ 0.5 which yields ne^-0.01n ≥ 50.

Then I'm stuck. Is this correct so far and any direction on where to go from here will be appreciated. Thanks.

I think you actually want P(X \geq 1) \geq 0.5, since a sample which contains more than one defective car contains a defective car.
 
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