Proving $\text{Var}{(\hat{y}_h)} = \sigma^2 \left(\frac{1}{n} + \frac{(x_h-\bar{x})^2}{S_{xx}}\right)$ I have asked in another question how $\text{Var}{(\hat{y}_h)} = \sigma^2 \left(\frac{1}{n} + \frac{(x_h-\bar{x})^2}{S_{xx}}\right)$.  Note that $\hat{y}_h$ = $b_0 + b_1X_h$ which is a regression line estimate at some given $X_h$.
This question concerns why the term $Cov(b_0,b_1)$ alone yields the RHS.  Substituting $b_0 = Y - b_1X$ we get that $Cov(Y,b_1) - XCov(b_1,b_1)$ = $Cov(\frac{\sum{Y_i}}{n},\sum k_iY_i) - XVar{(b_1)}$. Here X and Y without subscript are arithmetic means.
We can then rearrange to obtain $\sum \frac{k_i Var(Y_i)}{n} - \frac{X\sigma^2}{S_{xx}}$ which quickly yields the desired result.  My question is, why does this work?  This single term does not seem like it should alone yield the RHS.  Have I made an error in algebra?
 A: $(1)\ E(\hat{Y_h}) = E(b_0 + b_1X_h) = \beta_0 +\beta_1X_h$
$(2)\ var(\hat{Y_h}) = var(b_0 + b_1X_h)$
An alternate (but equivalent) version of the regression model can be written as:
$Y_i = \beta_0X_0 + \beta_1X_1 + \epsilon_i$
This model associates an X variable with each coefficient $(where X_0 = 1)$
Al alternate modification is to use the deviation $X_i -\bar{X}$ rather than $X_i$ 
So the model can be written as:
$Y_i = \beta_0^* + \beta_1(X_i - \bar{X}) + \epsilon_i$
where $(3)\ \beta_0^* = \beta_0 + \beta_1\bar{X}$
These models can be used interchangably.
We know from the normal equations:
$\Sigma Y_i = nb_0 + b_1\Sigma X_i$
solving for $b_0$
$(4)\ b_0 = \bar{Y} - b_1\bar{X}$
So substituting from (3) and (4):
$b_0^* = b_0 + b_1\bar{X} = (\bar{Y} - b_1\bar{X}) + b_1\bar{X} = \bar{Y}$
$(5)\ var(\hat{Y_h}) = var(b_0 + b_1X_h) = var(\bar{Y} + b_1(X_h - \bar{X}))$
using:
$var(\bar{Y}) = \frac{\sigma^2}{n}$
$var(aX) = a^2var(X)$
and 
$var(X + Y) = Var(X) + Var(Y) + 2Cov(X,Y)$
So:
= $var(\bar{Y}) +(X_h - \bar{X})^2var(b_1) + 2(X_h-\bar{X})cov(\bar{Y},b_1)$
we use the fact that $Cov(\bar{Y},b_1) = 0$ due to the i.i.d assumption on $\epsilon_i$
$= \frac{\sigma^2}{n} + (X_h-\bar{X})^2\frac{\sigma^2}{\Sigma(X_i-\bar{X})^2}$
$= \sigma^2[\frac{1}{n} + \frac{(X_h - \bar{X})^2}{\Sigma(X_i - \bar{X})^2}]$
A: Provided that
$$
\text{Var}(\hat{y}_{x_0}) = \displaystyle\frac{\sigma^2\sum x_i^2}{n\sum(x_i - \bar{x})^2} + \displaystyle\frac{\sigma^2x_0^2}{\sum(x_i - \bar{x})^2} - \displaystyle\frac{2x_0\sigma^2\bar{x}}{\sum(x_i - \bar{x})^2}
$$
The term, $\displaystyle\frac{\sigma^2\sum x_i^2}{n\sum(x_i - \bar{x})^2}$ is the most troublesome and can be broken down using basic arithmetic and knowing that playing with $\sum x_i^2$ is dangerous. So,
$$
\displaystyle\frac{\sigma^2(\sum x_i^2 + 2n\bar{x}^2 - 2n\bar{x}^2)}{n\sum(x_i - \bar{x})^2} \rightarrow \displaystyle\frac{\sigma^2(n\bar{x}^2 + \sum x_i^2 + \bar{x}^2\sum(1) - 2n\bar{x}\{\sum(x_i)/n\})}{n\sum(x_i - \bar{x})^2}
$$
Where $n = \sum(1)$ and $\bar{x} = \sum(x_i)/n$ which is basic.
$$
\displaystyle\frac{\sigma^2(n\bar{x}^2 + \sum(x_i^2 + \bar{x}^2 - 2\bar{x}x_i))}{n\sum(x_i - \bar{x})^2} \rightarrow \displaystyle\frac{\sigma^2(n\bar{x}^2 + \sum(x_i - \bar{x})^2)}{n\sum(x_i - \bar{x})^2}
$$
Here $\sum(x_i - \bar{x})^2 = S_{xx}$ so, our final term is
$$
\displaystyle\frac{\sigma^2(n\bar{x}^2 + S_{xx})}{nS_{xx}} \rightarrow \displaystyle\frac{\sigma^2\bar{x}^2}{S_{xx}} + \displaystyle\frac{\sigma^2}{n}
$$
So, our original equation becomes
$$
\text{Var}(\hat{y}_{x_0}) = \displaystyle\frac{\sigma^2\bar{x}^2}{S_{xx}} + \displaystyle\frac{\sigma^2}{n} + \displaystyle\frac{\sigma^2x_0^2}{S_{xx}} - \displaystyle\frac{2x_0\sigma^2\bar{x}}{S_{xx}}
$$
$$
\text{Var}(\hat{y}_{x_0}) = \sigma^2\left[\displaystyle\frac{1}{n} + \displaystyle\frac{\bar{x}^2 + x_0^2-2x_0\bar{x}}{S_{xx}}\right] \rightarrow \text{Var}(\hat{y}_{x_0}) = \sigma^2\left[\displaystyle\frac{1}{n} + \displaystyle\frac{(x_0 - \bar{x})^2}{S_{xx}}\right]
$$
