= Residual estimate of Gaussian noise variance
{title2=$\widehat\sigma^2=\operatorname{RSS}/(n-p)$}
= Residual mean square
{synonym}
For a correctly specified full-rank fixed-design Gaussian <linear regression> with $p<n$, dividing the <residual sum of squares> by the <residual degrees of freedom> $n-p$ gives an <unbiased estimator> of the noise <variance>. The projection removes the whole deterministic mean, so the numerator's expectation is $(n-p)\sigma^2$. A misspecified subset model need not have this property.
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