Bernoulli, Poisson & Normal Probability

In summary, the probability of a chocolate bar containing at least three but no more than seven deadly squares is 0.10.
  • #1
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[SOLVED] Bernoulli, Poisson & Normal Probability

Homework Statement

Every chocolate bar contains 100 squares, with 10% of the individual squares presenting a health hazard to people consuming them.

(a) Using the Binomial, Poisson and Normal distributions, write down formulas for
the probability that a single chocolate bar has at least 3 but no more than 7 deadly
squares.

The attempt at a solution

For the binomial part, I've just done Pr(x=3) + Pr(x=4) +... Pr(x=7). Just wondering if that's what you would do?

I've done the same for the Poisson, using μ = 10.

I'm stuck on Normal distribution though.
I'm thinking of using
http://img294.imageshack.us/img294/5079/untitledzi8.jpg
but I don't know σ.
 
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  • #2
You can look at this as a Bernoulli for each individual square with p=0.1 and q=0.9; then variance = pq for each square. That would be the first approximation. Then you need to formulate a way around the problem that squares in a given bar are mutually dependent; e.g. if you know the first 90 are safe then you know that the remaining 10 are deadly. At least that's how I interpret your question.
 
  • #3
Hmm, what do you suggest? I realize it's not as simple as the bernoulli and poisson where you can just add up the individual Pr's...
 
  • #4
When I re-read the question I realized that the question does not imply a dependence. If there can be as few as 3 deadly squares, then knowing that 90 are safe does not tell me the remaining 10 are deadly. This makes it much easier. Under binomial, mean = np = 10 and variance = npq = 9, which you can apply to a normal distribution.
 
  • #5
Yeah so std dev is 3, mean is 10
I just slap it into the above formula? Initially I thought I should integrate from 3 to 7, but it seems as though I should be doing from 2.5 to 7.5? Does this sound correct?
 
  • #6
edit: I am totally confused for normal dist now. Am I supposed to integrate at all or do I just use the formula I posted near the top? I mean to integrate that massive thing even though I know std dev and mean is out of my scope. Am I even on the right track?
 
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  • #7
The normal approximation to the binomial distribution for given p (q= 1-p) and n has mean pn and standard deviation [itex]\sqrt{np(1-p)}[/itex]. Use those in your formula for the standard z-score, and find the probability that x is between 2.5 and 7.5. (That's the "integer correction")
 
  • #8
Can you guide me through it? I'm so damn lost.
 
  • #9
So basically...

http://img520.imageshack.us/img520/1197/untitledzi8gt4.jpg
 
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  • #10
Correct; except the left hand side is FX(7.5) - FX(2.5).
 
  • #11
Thanks a bunch!
 

1. What is the Bernoulli probability distribution?

The Bernoulli probability distribution is a discrete probability distribution that models the probability of success or failure in a single experiment or trial. It is named after the Swiss mathematician, Jacob Bernoulli.

2. How is the Poisson distribution used in probability?

The Poisson distribution is a discrete probability distribution that is used to model the number of events that occur in a fixed interval of time or space. It is often used in situations where the events occur randomly and independently of each other.

3. What is the difference between the Poisson and Bernoulli distributions?

The main difference between the Poisson and Bernoulli distributions is that the Poisson distribution is used to model the number of events that occur in a fixed interval, while the Bernoulli distribution is used to model the probability of success in a single trial. Additionally, the Poisson distribution is a continuous distribution, while the Bernoulli distribution is a discrete distribution.

4. What is the Normal distribution and why is it important?

The Normal distribution, also known as the Gaussian distribution, is a continuous probability distribution that is commonly used to model real-world phenomena. It is important because of the Central Limit Theorem, which states that the sum of a large number of independent and identically distributed random variables will be approximately normally distributed.

5. How is the Normal distribution related to the Central Limit Theorem?

The Normal distribution is closely related to the Central Limit Theorem, as it is the limiting distribution for the sum of a large number of independent and identically distributed random variables. This means that as the sample size increases, the distribution of the sample mean will approach a Normal distribution, regardless of the underlying distribution of the individual variables.

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