# How to get the variance of the residual variance in a simple linear regression model

How do you get the variance of the residual variance in a simple linear regression model? In a book I see that ${\rm Var}(S_R^2)=\frac{2\sigma^4}{n-2}$, where $S_R^2=\sum\frac{e_i^2}{n-2}$ and $e_i=y_i-\hat{y}_i$. How can I get this result?

I already know that $\frac{\sum e_i^2}{\sigma^2}\sim\chi^2_{n-2}$, but I need to know how I get the variance of $S_R^2$.

• Could you add the self-study tag? I think it applies here. Feb 4 '15 at 20:24
• Hint: the variance of a $\chi^2_{n-2}$ distribution is $2(n-2)$. That's the only additional information you need.
– whuber
Feb 4 '15 at 20:24
• Have you visited the Wikipedia page on the $\chi^2$ distribution? It contains all the relevant items of information. Feb 4 '15 at 20:25
• @will198, if you've figured it out, why not answer your own question? Feb 4 '15 at 20:38
• Maybe you could post this as an answer instead of a comment. Feb 4 '15 at 20:51

• $\frac{\sum e_i^2}{\sigma ^2}\sim \chi^2_{n-2}$
• and knowing that $var(\chi^2_{n-2}=2(n−2)$
• I can get that $var[\frac{\sum e_i^2}{\sigma ^2}]=2(n−2)$ dividing for $(n−2)^2$ and multipliying for $\sigma^4$ I get $var[\frac{\sum e_i^2}{(n-2)}]=\frac{2\sigma^4}{n-2}=var[S^2_R]$. I applied that $a^2 \cdot Var(x)=Var[a \cdot x]$