Poisson distribution (would you please verify?)

In summary: Problem (1):Noting probability of Poisson distribution with p, the number of calls with x, and the mean with m, we havea) p=[(m^x)*(e^-m)]/x! = e^-m = e^-3b) x=3 => p=1-p(x=2) = 1- [(m^2)*(e^-m)]/2! = 1- [(3^2)*(e^-3)]/2Problem (2):Preserving the notation above, p=.99 , x=1 =>.99
  • #1
elmarsur
36
0

Homework Statement



1. Suppose that the number of telephone calls an operator receives from 9:00 to 9:05 A.M. follows a Poisson distribution with mean 3. Find the probability that the operator will receive:
a. no calls in that interval tomorrow.
b. three or more calls in that interval the day after tomorrow.


2. Find the number of chocolate chips a cookie should contain
on the average if it is desired that the probability of a cookie
containing at least one chocolate chip be .99.




Thank you very much for any help!


Homework Equations





The Attempt at a Solution


Problem (1):
Noting probability of Poisson distribution with p, the number of calls with x, and the mean with m, we have
a) p=[(m^x)*(e^-m)]/x! = e^-m = e^-3
b) x=3 => p=1-p(x=2) = 1- [(m^2)*(e^-m)]/2! = 1- [(3^2)*(e^-3)]/2

Problem (2):
Preserving the notation above, p=.99 , x=1 =>
.99 = [(m^x)*(e^-m)]/x! = m*(e^-m)
If so, do I take ln of both sides to find m?


Thank you very much for any help!
 
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  • #2
1a looks fine, but your notation could use some work. Using m = 3, P(X = 0) = m0e-m/0! = e-3
For 1b, it asks for the probability that X is >= 3, not just P(X = 3). P(X >= 3) = 1 - [P(X = 0) + P(X = 1) + P(X = 2)]
 
Last edited:
  • #3
Thank you very much, Mark!
Would you please extend your help?
1. I don't understand how I would arrive at e^3 instead of e^-3
2. Would you have any suggestions for Problem 2 (the cookie)?

Thank you to anyone who would help!
 
  • #4
It is e^(-3). I didn't carry the minus sign through from the previous step. (It's fixed, now.)
elmarsur said:
Find the number of chocolate chips a cookie should contain
on the average if it is desired that the probability of a cookie
containing at least one chocolate chip be .99.
P(X >= 1) = .99, so P(X = 0) = 1 - P(X >= 1) = 1 - .99 = .01
But P(X = 0) = .01
==> m0e-m/0! = .01
Can you solve this for m?
 
  • #5
Thanks Mark!
Before I saw your reply, this is what I did:

p = .99 + p(x=0) = .99 + [(m^0)*(e^-m)]/0! = .99 + e^-m = 1 (1 is total probability = certainty)

If so, then m = -ln(0.01)

How wrong am I?
 
  • #6
You're not wrong - that's correct. According to this problem, a cookie should have between 4 and 5 chocolate chips.
 
  • #7
Thank you very much, Mark, for all your help!
 
  • #8
Problems resolved.
 
  • #9
I hope you take heed of the guidance I was providing on writing notation so that it makes sense.
 
  • #10
Thank you again, Mark!
Good thing you refreshed the point; I had almost forgotten.
Of course, I see that it wouldn't make sense without specifying the P(x=0) how I arrive at the end form.

I hope you are around if I need help with other things in the future.

Thanks again, and best to you.
 
  • #11
You're welcome. I'm around here quite a bit, and when I'm not, there are lots of other very capable people who enjoy helping out.
Mark
 

Related to Poisson distribution (would you please verify?)

1. What is a Poisson distribution?

A Poisson distribution is a statistical distribution that represents the probability of a certain number of events occurring within a specific time period or space, given the average number of events that occur. It is often used to model rare events or phenomena that occur randomly and independently.

2. How is a Poisson distribution different from other distributions?

A Poisson distribution is unique in that it has only one parameter, the average number of events, while other distributions may have multiple parameters. Additionally, a Poisson distribution is discrete, meaning the possible outcomes are whole numbers, unlike continuous distributions which can have any value within a range.

3. What are the key assumptions of a Poisson distribution?

The key assumptions of a Poisson distribution are that the events occur independently, the probability of an event occurring is constant, and the events occur at a fixed rate. These assumptions are often referred to as the "Poisson assumptions."

4. How is the Poisson distribution used in real-world applications?

The Poisson distribution is commonly used in fields such as insurance, economics, and biology to model rare events such as accidents, natural disasters, or disease outbreaks. It can also be used to analyze data from experiments and surveys, where the number of occurrences of a particular event is of interest.

5. Can the Poisson distribution be approximated by other distributions?

Yes, the Poisson distribution can be approximated by other distributions, such as the normal distribution or the binomial distribution, under certain conditions. This is known as the Poisson approximation and is often used in cases where the Poisson assumptions are not met or the data is not discrete.

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