Finding Variance of Sample Mean of Poisson Distribution

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Homework Help Overview

The discussion revolves around finding the variance of the sample mean of a Poisson distribution, focusing on the properties and definitions related to variance in this context.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants explore definitions of variance and question how to apply them to the sample mean of a Poisson distribution. There are attempts to clarify the relationship between the variance of a random variable and the sample variance.

Discussion Status

Some participants have provided insights into the definitions of variance and sample variance, while others express confusion about specific terms and concepts. Multiple interpretations of the variance in the context of the Poisson distribution are being explored.

Contextual Notes

Participants note a lack of resources and guidance in their textbooks regarding the topic, which contributes to their uncertainty and need for clarification.

EvLer
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How do I approach finding variance of sample mean of Poisson distribution?
thanks.
 
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EvLer said:
How do I approach finding variance of sample mean of Poisson distribution?
thanks.

You can certainly start with looking at the definition of variance.
 
I did: Var(X) = E(X2) - (E(X))2
the problem is that this is given for rv X, not X' or X-bar, that's where i get lost.
I know that last term is lambda2.
Actually what I need to find for this problem is E(X'2), i.e.
E(X'2) = Var(X') + lambda2
 
Last edited:
ok, last question, pleeeease some one look :redface: i can't find much on this anywhere and the book does not say much... i don't have intuition for these things...
can I say that sample variance is sum(lambda)/n?
I found something that said sample variance is Sum(Var[X]) of whatever it is the RV divided by n...
 
Last edited:
According to my old lecture notes, the sample variance for (X1, ..., Xn) is defined with [tex]\frac{1}{n} \sum_{i=1}^n(X_{i}-\overline{X})^2[/tex], where [tex]\overline{X} = \frac{1}{n}\sum_{i=1}^n X_{i}[/tex] , and, in your case X~P(lambda).
 
Yes, the Poisson distribution depends on a single parameter and has the property that both mean and standard distribution are equal to that parameter.
 

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