Probability - Binominal distribution

In summary, we are trying to find the probability that a randomly chosen bottle that survived four drops is defective. Using Bayes Theorem, we can calculate this by first finding the probabilities of a bottle surviving four drops given that it is defective (P(S:D)), surviving four drops given that it is not defective (P(S:!D)), the overall probability of a bottle being defective (P(D)), and the overall probability of a bottle surviving four drops (P(S)). From there, we can use these values to calculate the probability of a bottle being defective given that it survived four drops (P(D:S)).
  • #1
MarcMTL
26
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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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  • #2
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..
 
  • #3
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).
 

FAQ: Probability - Binominal distribution

What is the definition of binomial distribution?

Binomial distribution is a probability distribution that describes the likelihood of obtaining a certain number of successes in a fixed number of independent trials with only two possible outcomes (success or failure) and a constant probability of success for each trial.

What are the characteristics of binomial distribution?

There are three main characteristics of binomial distribution: there are a fixed number of trials, each trial has only two possible outcomes, and the probability of success remains constant for each trial.

How is binomial distribution different from normal distribution?

Binomial distribution is different from normal distribution in several ways. First, binomial distribution is discrete while normal distribution is continuous. Second, binomial distribution has a fixed number of trials while normal distribution can have an infinite number of trials. Finally, binomial distribution describes the likelihood of a certain number of successes while normal distribution describes the likelihood of a range of values.

What is the formula for calculating binomial distribution?

The formula for calculating binomial distribution is P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, p is the probability of success, and nCx is the number of combinations of x successes in n trials.

How is binomial distribution used in real life?

Binomial distribution is used in real life to model and analyze situations with only two possible outcomes, such as flipping a coin, rolling a die, or conducting surveys. It helps in predicting the likelihood of a certain number of successes or failures in a given number of trials, and is used in various fields such as finance, biology, and sports.

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