Probability - Binominal distribution

AI Thread Summary
The discussion revolves around calculating the probability that a randomly selected glass bottle is defective after it survives four drops. Given that 15% of the bottles are defective and that defective bottles break 60% of the time, the probability of a defective bottle surviving four drops is calculated as 2.56%. The user initially miscalculates the overall probability by incorrectly combining probabilities. The suggestion is to apply Bayes' Theorem to find the conditional probability of the bottle being defective given that it survived, emphasizing the need to consider the entire population of bottles and their respective survival rates. This approach will lead to the correct probability assessment.
MarcMTL
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Homework Statement


A factory makes glass bottles, and 15.0% of the bottles are considered defective. A defective bottle will break 60.0% of the time when dropped from a controlled height, whereas a non-defective bottle will only break 10.0% of the time in the same conditions.

You pick a single bottle at random and drop it four times in a row. It doesn't break. What is the probability that it is a defective bottle?

Homework Equations


The Attempt at a Solution



If a defective bottle breaks 60% of the time, I calculated the probability of it surviving four consecutive drops: (1-0.60)4 = 0.0256 (2.56%)

This is where I get doubtful, I'd tend to say that if 20% of the bottles are defective, and we picked a single bottle at random, that the probability that the bottle is defective would be:

0.0256 * 0.20 = 0.00512 (0.512%)

Not the right answer. Also, I feel that the last step was incorrect.

Any help would be greatly appreaciated!

Marc
 
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Try thinking about the whole population of bottles.

X% are not defective.
Y% are defective.

Now you pick a bottle at random and drop it four times.
What can happen?

It could be defective and survive. (a%)
It could be not-defective and survive (b%)
It could be defective and break (c%)
If could be not-defective and break (d%)

Now of course a+b+c+d = 100%. And we know that what actually happened was that our bottle didn't break, so we are actually only looking at (a+b)% of the population.

That should get you started..
 
Another way is...

S: the bottle survived 4 drops
D: the bottle is defective

Find these probabilities:
P(S:D), P(S:!D), P(D), P(S).

Then use Bayes Theorem to find P(D:S).
 
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