A poisson distribution question

In summary, the average number of eggs found on daily visits to a henhouse with 18 hens laying 1 egg every 30 days is 0.6 eggs. The most probable number of eggs found on each visit is 0. The probability of finding 0 eggs is represented by P(0) in the Poisson distribution, which has values for N ranging from 0 to infinity. The probability of finding no eggs is 0.549. The distribution can be graphed with probability on the y-axis and number of eggs on the x-axis.
  • #1
8614smith
54
0

Homework Statement


On average, each of the 18 hens in my henhouse lays 1 egg every 30 days. If I check the hens once per day and remove any eggs that have been laid, what is the average number, μ, of eggs that I find on my daily visits? What is the most probable (whole) number of eggs that I find on each visit? HINT: if in doubt sketch the distribution of P(N) in this case.


Homework Equations



[tex]P_{\mu}(N)=\frac{e^{-\mu}{\mu}N}{N!}[/tex]


The Attempt at a Solution



18 hens lay 1 egg every 30 days so average, [tex]\mu[/tex], is 0.6 eggs a day.

Number of occurences, N, is 1 as it is checked once a day.

so P_[tex]\mu[/tex](N) = 0.329 using the numbers given.

And the most probable whole number of eggs found on each visit is 0.

Is this correct??
 
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  • #2
No, you made a mistake regarding what N stands for. N is the number of eggs you could find on a visit. P(0) is the probability of finding no eggs; P(1) is the probability of finding one egg; and so on.
 
  • #3
8614smith said:
On average, each of the 18 hens in my henhouse lays 1 egg every 30 days.

These are some remarkably unproductive hens! In my experience, a more typical egg production rate would be closer to an egg per day for each hen.
 
  • #4
so how do i find the probability of finding 0 eggs because if i put zero in the top line it will be 0, and that 0.329 is the probability of finding one egg then.
 
  • #5
Oh, your formula is wrong. I thought it was just a typo. It should be

[tex]P(N)=e^{-\mu}\frac{\mu^N}{N!}[/tex]
 
  • #6
ok great thanks makes sense now, but how do i draw the poisson distribution? can it be 0.549 probability at zero? for it to be continuous it would have to have a negative distribution - is that possible? I am assuming probability on the y-axis and number of eggs on the x-axis by the way
 
  • #7
Look up the Poisson distribution in your textbook. It should answer your questions, like what the possible values of N are.
 

FAQ: A poisson distribution question

What is a Poisson distribution?

A Poisson distribution is a statistical distribution that is used to model the probability of a certain number of events occurring within a specific time period or space, when the average number of events is known.

What are the characteristics of a Poisson distribution?

The characteristics of a Poisson distribution include a discrete random variable, a fixed number of trials, events that are independent of each other, and a constant probability of success. It also has a mean and variance that are equal.

How is a Poisson distribution different from a normal distribution?

While both are statistical distributions that describe the probability of certain events, a Poisson distribution is used for discrete data while a normal distribution is used for continuous data. Additionally, a normal distribution has a symmetrical bell shape while a Poisson distribution has a skewed shape.

What are some real-life examples of where a Poisson distribution is used?

A Poisson distribution can be used to model the number of customers entering a store in a given time period, the number of car accidents in a certain area, or the number of calls received by a customer service center in a day. It is also commonly used in the fields of epidemiology, finance, and telecommunications.

How is a Poisson distribution calculated?

The Poisson distribution formula is: P(x;λ) = (e^-λ) (λ^x) / x!, where P is the probability of x events occurring, λ is the average number of events, and x is the number of events. This can also be calculated using statistical software or tables.

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