Two-dimensional sufficient statistic for OLS model 
Suppose that the random variables $Y_1,...,Y_n$ satisfy $$Y_i=\beta x_i + \epsilon_i, i=1,...,n$$ where $x_1,...,x_n$ are fixed constants, and $\epsilon_1,...,\epsilon_n$ are i.i.d. $N(0,\sigma^2), \sigma^2$ unknown.
(a) Find a two-dimensional sufficient statistic for $(\beta, \sigma^2)$.

I have no idea where to even start with this one.  Prof didn't do any examples like this in class.  Any help would be appreciated.
 A: Let's compute the likelihood function for this model. Since $Y_i=\beta x_i+\epsilon_i\sim\mathcal{N}(\beta x_i,\sigma^2)$:
$$\mathcal{L}(\beta,\sigma^2)=\prod_{i=1}^n \frac{1}{\sqrt{2\pi\sigma²}}\exp\left(-\frac{(y_i-\beta x_i)^2}{2\sigma^2}\right)\\
=\frac{1}{({2\pi\sigma^2})^\frac{n}{2}}\exp\left(\frac{1}{2\sigma^2}\sum_{i=1}^n (y_i-\beta x_i)^2\right)\\
=\frac{1}{({2\pi\sigma^2})^\frac{n}{2}}\exp\left(\frac{1}{2\sigma^2}\sum_{i=1}^n y_i^2-\frac{1}{2\sigma^2}\sum_{i=1}^n2\beta x_iy_i+\frac{1}{2\sigma^2}\sum_{i=1}^n\beta^2x_i^2\right)\\
=\frac{1}{({2\pi\sigma^2})^\frac{n}{2}}\exp\left(\frac{1}{2\sigma^2}\sum_{i=1}^n y_i^2\right)\exp\left(-\frac{\beta}{\sigma^2}\sum_{i=1}^nx_iy_i\right)\exp\left(\frac{\beta^2}{2\sigma^2}\sum_{i=1}^nx_i^2\right)$$
Now, since the likelihood function can be factorized in terms that depend on the sample only through $\sum_{i=1}^ny^2$ and $\sum_{i=1}^nx_iy_i$, we can affirm that $T(Y)=\left(\sum_{i=1}^nx_iY_i,\sum_{i=1}^nY_i^2\right)$ is a sufficient statistic.
