1. ## Unbiased estimators

So I'm revising my stats by doing a past exam paper, and I'm stuck on one of the questions about unbiased estimators. The question goes...

Suppose that $X_1$, $X_2$,..., $X_n$ are a random sample from a population with mean $\mu$ and variance $\sigma^2$.
a) Prove that the mean estimator $\bar{X}$ is unbiased for $\mu$. State carefully any formulae you use.
b) Prove that $\bar{X}$ has variance $\frac{\sigma^2}{n}$. State carefully any formulae you use.
It's probably really simple like the last unbiased estimator Q I posted a while back, but I'm just stumped.

2. Originally Posted by chella182
So I'm revising my stats by doing a past exam paper, and I'm stuck on one of the questions about unbiased estimators. The question goes...

Suppose that $X_1$, $X_2$,..., $X_n$ are a random sample from a population with mean $\mu$ and variance $\sigma^2$.
a) Prove that the mean estimator $\bar{X}$ is unbiased for $\mu$. State carefully any formulae you use.
b) Prove that $\bar{X}$ has variance $\frac{\sigma^2}{n}$. State carefully any formulae you use.

It's probably really simple like the last unbiased estimator Q I posted a while back, but I'm just stumped.
$\overline{X}=\frac{1}{n}\sum_{i=1}^n X_i$

By linearity of the expectation operator:

$E(\overline{X})=\frac{1}{n}\sum_{i=1}^n E(X_i)$

but by definition $E(X_i)=\mu$, so:

$E(\overline{X})=\frac{1}{n}\sum_{i=1}^n \mu=\mu$

Which is the definition of an unbiased estimator for $\mu$.

CB

3. Thank you. Does sound rather simple, I just find it difficult to get my head around this sort of stuff.