Poisson distribution probability problem

In summary, the probability of no errors is 0.00413 and the probability of one or more errors is 7-12.
  • #1
Seneka
41
0
Homework Statement
The number of mistakes a teacher makes while marking homework has a poisson distribution with mean of 1.6 errors per piece of homework. Find the probability that in a class of 12 pupils fewer than half of them have errors in their marking.
Relevant Equations
P(X=r)= (e to the power - lambda)(lamba to the power r) all divided by r factorials
where lamba is the average rate of the event.
the expected and variance are both equal to the average rate of the event.
Mentor edit: ##P(X = r) = e^{-\lambda}\frac{\lambda^r}{r!}##
LaTeX script: # #P(X = r) = e^{-\lambda}\frac{\lambda^r}{r!}# #
So I thought you would find the probability of having 0 errors when the mean rate is 1.6. Square that by 5 and multiply that by one minus the probability of having 0 errors to the power of 7. So that is basically the probability of having 0 errors to the power of 5 multiplied by the probability of having one or more errors to the power of 7 for the 7 students that do get errors in their marking. This gave me 6.919...x10 to the power -5 which is not the answer.
The answer is 0.00413.
 
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  • #2
First, you are calculating the probability that exactly 5 have no errors. The question asks for the probability that fewer than half have errors, that is the total probability that the number with no errors is from 7 to 12.
Second, your calculation is wrong.
 
  • #3
Sorry, some problem posting reply. Your calculation for 5 with no errors is wrong - which 5? You need to multiply by the number of combinations of 5 from 12.
 
  • #4
mjc123 said:
Sorry, some problem posting reply. Your calculation for 5 with no errors is wrong - which 5? You need to multiply by the number of combinations of 5 from 12.

Are you suggesting doing a cumulative binomial distribution up to 5 where the probability of no error is e to the power -1.6?
 
  • #5
No - from 7 to 12. Read the question - fewer than half have errors. I mentioned 5 because that was the calculation you did, but you need 7 to 12.
 
  • #6
mjc123 said:
No - from 7 to 12. Read the question - fewer than half have errors. I mentioned 5 because that was the calculation you did, but you need 7 to 12.
Yeah I realized but now I am getting 2.03..x10 to the power -34
My calculation as 12choose7(e to the power -1.6) to the power 7 multiplied by (1-(e to the power -1.6)) to the power of 5 and carried this pattern until I came to 12...is this wrong?
 
  • #7
Seneka said:
Yeah I realized but now I am getting 2.03..x10 to the power -34
My calculation as 12choose7(e to the power -1.6) to the power 7 multiplied by (1-(e to the power -1.6)) to the power of 5 and carried this pattern until I came to 12...is this wrong?
I think it was an error. I got the right answer when I used my calculator to find the probability of less than and equal to 6 no errors and then subtracting from 1.
 
  • #8
mjc123 said:
No - from 7 to 12. Read the question - fewer than half have errors. I mentioned 5 because that was the calculation you did, but you need 7 to 12.
Thanks
 
  • #9
That is how you should do it, but I don't know what you're getting wrong. I get 0.0035 just for n = 7.
 
  • #10
mjc123 said:
That is how you should do it, but I don't know what you're getting wrong. I get 0.0035 just for n = 7.
Dw I got it, thanks!
 

1. What is the Poisson distribution probability problem?

The Poisson distribution probability problem is a statistical concept used to calculate the probability of a certain number of events occurring within a specified time or space, given a known average rate of occurrence. It is often used in situations where the events are independent and occur at a constant rate, such as in the field of biology or finance.

2. How do you calculate the Poisson distribution probability?

The Poisson distribution probability can be calculated using the formula P(x) = (e^-λ * λ^x) / x!, where x is the number of events, λ is the average rate of occurrence, and e is the mathematical constant approximately equal to 2.71828. This formula can be used to find the probability of a specific number of events occurring, or to create a probability distribution for a range of possible events.

3. What are the assumptions of the Poisson distribution probability problem?

The Poisson distribution probability problem assumes that the events are independent and occur at a constant rate. It also assumes that the probability of an event occurring in a given time or space is proportional to the length of that time or space. Additionally, it assumes that the events are rare, meaning that the average rate of occurrence is relatively small compared to the total time or space being considered.

4. What types of real-world problems can be modeled using the Poisson distribution probability?

The Poisson distribution probability can be used to model a wide range of real-world problems, such as the number of customers arriving at a store, the number of accidents on a highway, or the number of bacteria in a petri dish. It can also be used in financial modeling, such as predicting the number of insurance claims or the number of defects in a manufacturing process.

5. How does the Poisson distribution probability differ from the binomial distribution?

The Poisson distribution probability is used to model the number of events that occur in a given time or space, while the binomial distribution is used to model the probability of a certain number of successes in a series of independent trials. The Poisson distribution assumes a constant rate of occurrence, while the binomial distribution allows for varying probabilities of success in each trial. Additionally, the Poisson distribution is used for rare events, while the binomial distribution is used for more common events.

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