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?

This is how you would calculate the sum of squared residuals -

*

*Use the value of your coefficients to come up with a prediction for all 100 values, $\hat{y}$


*Next compute the difference, for all observations, between the real $y$ and predicted value $\hat{y}$, $y - \hat{y}$


*Sum the square of those differences $\sum_{i=1}^{i=100}(y_i - \hat{y_i})^2$
