Poisson Probability: At Least 50% Defective Brake Lights

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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There are two things I don't understand about this problem. First, when finding the nth root of a number, there should in theory be n solutions. However, the formula produces n+1 roots. Here is how. The first root is simply ##\left(r\right)^{\left(\frac{1}{n}\right)}##. Then you multiply this first root by n additional expressions given by the formula, as you go through k=0,1,...n-1. So you end up with n+1 roots, which cannot be correct. Let me illustrate what I mean. For this...
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