Statistics Question - Expected value of an estimator

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Homework Help Overview

The discussion revolves around the expected value of an estimator for the population mean in statistics, specifically focusing on the estimator \(\bar{Y}\) and its relationship to the population mean \(\mu\). Participants are exploring the reasoning behind why the expected values of individual components of the estimator are equal to the population mean.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants are questioning the equality of the expected values of the individual samples \(E(Y_{i})\) to the population mean \(\mu\). There is an exploration of whether the samples \(Y_i\) are representative of the population and how this relates to the definition of the population mean.

Discussion Status

The discussion is ongoing, with participants providing clarifications and raising further questions about the nature of the samples and their relationship to the population mean. Some guidance has been offered regarding the reasoning behind the expected values, but no consensus has been reached.

Contextual Notes

There is uncertainty regarding the definition and characteristics of the samples \(Y_i\), including whether they are drawn from the same population and how this affects their expected values. Participants are also considering the implications of homework constraints on the discussion.

michonamona
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Hello friends!

Given an estimator of the population mean:

\bar{Y}=\frac{\sum^{N}_{i=1}Y_{i}}{N}

The expected value of \bar{Y} is :

E(\bar{Y}) = \frac{1}{N}E(Y_{1})+\frac{1}{N}E(Y_{2})+\cdots+\frac{1}{N}E(Y_{N})=\mu where \mu is the population mean.

Therefore:

E(\bar{Y}) = \frac{1}{N}\mu+\frac{1}{N}\mu+\cdots+\frac{1}{N}\mu


My question is, why are E(Y_{1}), E(Y_{2}), E(Y_{N}) all equal to \mu?
 
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michonamona said:
My question is, why are E(Y_{1}), E(Y_{2}), E(Y_{N}) all equal to \mu?

Isn't that exactly what is meant by "\mu is the population mean"?

By the way, you are missing a factor of 1/N in the definition of the estimator.
 
jbunniii said:
Isn't that exactly what is meant by "\mu is the population mean"?

By the way, you are missing a factor of 1/N in the definition of the estimator.

Thank you for your reply.

My mistake, I edited the equation.

But that's exactly what my question is about. If the Expected value of Ybar is equal to mu, then why is the expected value of EACH of the components of the series of Ybar also mu?

It must be something really simple that I'm missing...

Thanks
M
 
michonamona said:
Thank you for your reply.

My mistake, I edited the equation.

But that's exactly what my question is about. If the Expected value of Ybar is equal to mu, then why is the expected value of EACH of the components of the series of Ybar also mu?

It must be something really simple that I'm missing...

Thanks
M

Well, what ARE these components Y_i? I assume they are random samples from the population, are they not?

I think you are arguing in reverse. The expected value of the estimator is \mu BECAUSE the expected value of each of the random samples is \mu, not the other way around.
 
...expected value of each of the random samples is \mu, not the other way around.


so each of the random sample Y_i was taken from the population? meaning the size of each Y_i is the same as the population?


Thank you,
M
 
michonamona said:
so each of the random sample Y_i was taken from the population?

I don't know. I assume so, but you're the one who asked the original question. Is it homework? If so, doesn't the homework problem tell you what the Y_i's are?

meaning the size of each Y_i is the same as the population?

I don't know what you mean by "size." If each Y_i comes from the same population (more formally, the same probability distribution) then the STATISTICS should be the same for each Y_i. The mean (\mu) is one such statistic.
 

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