I am currently struggling on the meaning of the variance term $\sigma$ in the equation for computing the variance and the confidence interval of the mean reponse for a MLR:
$$
Var[\hat{y}(x\_0)]=\sigma^2\cdot x\_0'(X'X)^{-1}x\_0
$$
and
$$
\hat{y}(x\_0) - t\_{\alpha /2, df(error)}\sqrt{\hat{\sigma}^2\cdot x\_0'(X'X)^{-1}x\_0} 
$$
$$
\leq \mu\_{y|x\_0} \leq \hat{y}(x\_0) + t\_{\alpha /2, df(error)}\sqrt{\hat{\sigma}^2\cdot x\_0'(X'X)^{-1}x\_0} .
$$
(both formulas are taken from Myers, Montgomery, Anderson-Cook, "Response Surface Methodology" fourth edition, page 33-34)

1) Does $\sigma^2$ represents the variance of my data, or does it represent the variance of my Errors ?

Usually one uses the MSE as an estimator for $\sigma^2$, thus using the variance of my errors and not only of the data.

2) If I have already done a Measuring System Analysis (MSA), can I use the value of the calculated variance instead of the MSE of the Regression, because it would be a better estimator ?