Probability (binomial Distro?)

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In summary: P(k success in n trials with average probability p) = f(n,k,p)here the binomial approximation is p = 15/250, failure rate n = 6, samples k = 0 or 1 observed failures then the probabiltiy that the sample is accepted is P(lot failures <=1) = f(0,6,p) + f(1,6,p)f(0,6,p) is just the proability of getting no failures from 6 selected items with constant p f(1,6,p) is just the pro
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probabilityst
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Hi Everyone, I've been having some trouble with this problem:

A lot of 250 items that contains 15 defective items is subject to an acceptance sampling plan that calls for a smple of size 6 to be drawn(without replacement). the lot is accepted if the sample contains, at most, one defective item. find the probability that the lot is accepted.


The way I thought to do it was:
total number of samples that would be able to pass/total number of sample possible = (250-14)C6 / 250C6.

A friend of mine said that it should be done like this: (6c0) * 0.06^0 * (0.94)^6 + 6c1 * (0.06^1) * (0.94)^5, and something about it being a binomial distribution, but I don't know why/what he's saying.

Any help would be greatly appreciated.
 
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  • #2
note the binomial assumes a constant probabilty of success (failure). As p will effectively change as failed items are removed from the universe without replacement it is not exactly right but will be very close.

see binomial distribution http://en.wikipedia.org/wiki/Binomial_distribution
P(k success in n trials with average probability p) = f(n,k,p)

here the binomial approximation is
p = 15/250, failure rate
n = 6, samples
k = 0 or 1 observed failures

then the probabilty that the sample is accepted is
P(lot failures <=1) = f(0,6,p) + f(1,6,p)

note
f(0,6,p) is just the proability of getting no failures from 6 selected items with constant p
f(1,6,p) is just the proability of getting 1 failure from 6 selected items with constant p

I would calculate the probabilties by hand assuming no replacement and compare against the binomial case
 
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  • #3
probabilityst said:
Hi Everyone, I've been having some trouble with this problem:

A lot of 250 items that contains 15 defective items is subject to an acceptance sampling plan that calls for a smple of size 6 to be drawn(without replacement). the lot is accepted if the sample contains, at most, one defective item. find the probability that the lot is accepted.


The way I thought to do it was:
total number of samples that would be able to pass/total number of sample possible = (250-14)C6 / 250C6.

A friend of mine said that it should be done like this: (6c0) * 0.06^0 * (0.94)^6 + 6c1 * (0.06^1) * (0.94)^5, and something about it being a binomial distribution, but I don't know why/what he's saying.

Any help would be greatly appreciated.

The appropriate distribution is the hypergeometric. See, for example,

http://en.wikipedia.org/wiki/Hypergeometric_distribution
 
  • #4
nice point, couldn't remember the distribution
 

FAQ: Probability (binomial Distro?)

What is Probability?

Probability is a measure of the likelihood of an event occurring. It is represented by a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty.

What is a Binomial Distribution?

A binomial distribution is a probability distribution that describes the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. It is characterized by two parameters: the number of trials and the probability of success.

How is the Binomial Distribution Calculated?

The binomial distribution can be calculated using the formula P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial.

What is the Difference Between a Binomial Distribution and a Normal Distribution?

The main difference between a binomial distribution and a normal distribution is that a binomial distribution is discrete, while a normal distribution is continuous. This means that the values in a binomial distribution can only be whole numbers, while the values in a normal distribution can be any real number.

How is the Binomial Distribution Used in Real Life?

The binomial distribution has many real-life applications, such as in quality control, market research, and genetics. It is also commonly used in gambling and sports betting to predict the outcomes of various events.

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