Simple linear regression exercise I'm trying to do an exercise but I can't figure out how to proceed. OLS is run for this model, with 100 observations.
$$
y_i=b_0+b_1x_i+\epsilon_i
$$
The results are $$
\hat\beta =\begin{pmatrix} 9 \\1 \end{pmatrix}
$$
$$
V(\hat\beta)=\hat\sigma^2 (X'X)^{-1}=\begin{pmatrix} 3 &0.12 \\ 0.12 &0.064  \end{pmatrix}
$$
It asks me to calculate $$ e'e$$ aka the sum of squared residuals. I don't know what to do. I know in simple regressione the coefficients and variances have formulas that have to do with averages and variances of y and x. But I don't have any info on the individual observations. Could anyone tell me what I'm missing or how to solve this?

Notice that
$X'X = \begin{pmatrix}
n & \sum x_i\\
\sum x_i & \sum x_i^2 
\end{pmatrix}$
since you know $n$, there are three unknowns that determine the elements of the matrix $\text{Var}(\hat \beta)$  - $\sum x_i, \sum x_i^2$ and $\hat\sigma^2$. Use the three indpendent elements of the matrix to find those quantities, and then assuming
$$\hat\sigma^2 = \frac{e'e}{n-2}$$
you can find $e'e.$
