Expected value of squared sample mean

Click For Summary
The expected value of the squared sample mean, E[Ŷ²], is expressed as E[Ŷ²] = σ²/n + μ², where σ² is the population variance and μ is the population mean. The mean of the sample mean is equal to the population mean (μ), while its variance is the population variance (σ²) divided by the sample size (n). This relationship is derived from the general formula E[Y²] = Var(Y) + μ_Y², applicable to any random variable Y. Understanding these properties is crucial for statistical analysis involving sample means. The discussion highlights the foundational concepts of mean and variance in relation to sample statistics.
safina
Messages
26
Reaction score
0
May I ask how come that E[\bar{X}^{2}] = \frac{\sigma^{2}}{n} + \mu^{2}?
 
Last edited:
Physics news on Phys.org
Remember that for any random variable Y

<br /> E[Y^2] = Var(Y) + \mu_Y^2<br />

What do you know about the mean and variance of the sample mean?
 
statdad said:
Remember that for any random variable Y

<br /> E[Y^2] = Var(Y) + \mu_Y^2<br />

What do you know about the mean and variance of the sample mean?

Okey, the mean of the sample mean is mu and the variance of the sample mean is sigma squared divided by n.

Thsnk you!
 
If there are an infinite number of natural numbers, and an infinite number of fractions in between any two natural numbers, and an infinite number of fractions in between any two of those fractions, and an infinite number of fractions in between any two of those fractions, and an infinite number of fractions in between any two of those fractions, and... then that must mean that there are not only infinite infinities, but an infinite number of those infinities. and an infinite number of those...

Similar threads

Replies
5
Views
5K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 42 ·
2
Replies
42
Views
5K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 9 ·
Replies
9
Views
2K
  • · Replies 12 ·
Replies
12
Views
2K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 3 ·
Replies
3
Views
1K