Poisson Distribution: finding the MEan

In summary, a store opens at 8 in the morning and from 8 to 10, customers arrive at a poisson rate of 6 per hour. Between 10 and 12, the arrival rate increases to 10 per hour. The store closes for lunch from 12 to 2. From 2 to 5, the arrival rate decreases linearly from 10 per hour at 2 to 4 per hour at 5. The mean number of customers that enter the store on a given day can be calculated by taking the mean rate of 6 customers per hour for the first 2 hours, adding the mean rate of 10 customers per hour for the next 2 hours, and taking the mean rate of
  • #1
Niall101
20
0
A store opens at 8 in the morning. from 8 until 10 customers arrive at poisson rate 6 per hour. Between 10 and 12 they arrive at a poisson rate of 10 per hour. From 12 to 2, the store closes for lunch, Finally from 2 to 5 the arrival rate drops linearly from 10 per hour at 2 to four per hour at 5. Determin the mean number of customers that enter the store on a given day.

Firstly I am not sure what to do here except maybe from 2 to 5 we can say the Mean rate is 7 per hour?

Thanks in advance!
 
Physics news on Phys.org
  • #2
Try a smaller problem first. If the store is open for two hours, from 8 to 10, and customers arrive at poisson rate 6 per hour, can you calculate (or at least guess) the mean number of customers that arrive during these two hours?
 
  • #3
Hi I assume it would be 12. 2 x 6?

I have an answer for this question now. I just took the mean per hour and for the last 3 hours took the mean to be 7 and I got 53. It just seams too easy. Its the last question on a problem sheet and the first 5 were much more difficult and more involved.

Ps. Thanks your your kind reply.
 

What is Poisson Distribution?

Poisson Distribution is a statistical concept used to model the probability of rare events occurring over a specific time interval or in a defined area. It is often used in fields such as finance, biology, and engineering.

What is the mean of a Poisson Distribution?

The mean of a Poisson Distribution is also known as the lambda (λ) value. It represents the average number of occurrences of the rare event in the given time interval or area.

How do you find the mean of a Poisson Distribution?

To find the mean of a Poisson Distribution, you can use the formula λ = np, where n is the number of trials or observations and p is the probability of success in each trial. Alternatively, you can also calculate the mean using the formula λ = ∑ (x * f(x)), where x represents the number of occurrences and f(x) is the corresponding probability of that number of occurrences.

What are some real-world examples of Poisson Distribution?

Poisson Distribution can be used to model the number of customers entering a store in a given hour, the number of car accidents in a specific area, or the number of calls a customer service representative receives in a day. It can also be applied to natural phenomena such as the number of earthquakes in a region or the number of births in a hospital in a month.

What are the assumptions of Poisson Distribution?

There are a few assumptions that must be met for Poisson Distribution to accurately model a situation. These include a fixed time interval or area, independence between occurrences, and a low probability of multiple occurrences happening at the same time. Additionally, the mean and variance should be approximately equal for the distribution to be a good fit.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
903
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
12
Views
2K
  • Calculus and Beyond Homework Help
Replies
8
Views
671
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
15
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
4K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
Back
Top