[hessenwolf]

It looks like an enthusiastic newbie with some clipart and an equation. Fair play for trying.

I would suggest adding the following.

1. What the poster above said.

2. The reason for the E[(x_{bar} - x_i)^2] choice. Why not E[|x_{bar} - x_i|]? Was it a mathematical convencience? Was it, perhaps, because Gauss had the integral of e_{t^2} from -Inf to plus Inf lying around in a letter from Laplace?

3. It is an equation with a square. Use a square somewhere.

4. The square root of the variance happens to be the horizontal distance between the mean and the point of inflection in the normal distribution. How cool is that?

[mturmon]

I like (3) in particular. You could introduce, in a very simple way, the idea that the "error" (X - E X) is perpendicular to the "estimate" (E X). That's the two legs of the right triangle; the hypotenuse is "X" itself.