how to find the distribution of the least square estimator $\beta$? i'm solving a problem that involve a linear model, and i'm trying to get the distribution of the least square estimator $\beta$.
i found in a book that: 

$\widehat{\beta}\sim N_{p}(\beta, (X^{\prime}X)^{-}\sigma^{2})$. but how can i get the distributions to $\beta_{i}$?.

in my case, the matrix $(X^{\prime}X)^{-}$, i found was: 
$$ \frac 1 \alpha
\begin{bmatrix} 
 \sum_{i=1}^N x_i^2 &  -\sum_{i=1}^N x_i \\
  &  \\
 -\sum_{i=1}^N x_i & N\\
\end{bmatrix}
$$
where $\alpha= N \sum_{i=1}^N x_i^2- \left[ \sum_{i=1}^N x_i \right]^2$
and i'm looking for the distribution of $\beta_1, \beta_2$.
 A: The result that you found
$$
\hat\beta\sim N_p(\beta, (X'X)^{-1}\sigma^2)\tag{*}
$$ allows you to read off the solution to your problem. If your model is
$$Y_i = \beta_1 + \beta_2x_i+\varepsilon_i,\qquad i=1,\ldots,N,
$$
then interpret the assertion (*) to mean that the joint distribution of $(\hat\beta_1,\hat\beta_2)$ is bivariate normal ($p=2$) with mean $(\beta_1,\beta_2)$ and covariance matrix $(X'X)^{-1}\sigma^2$. So the marginal distribution of $\hat\beta_1$ is normal with mean $\beta_1$ and variance equal to the $(1,1)$ entry of the covariance matrix, which is
$$
\frac{\sigma^2}{N\sum x_i^2-(\sum x_i)^2}\sum x_i^2=\sigma^2\frac{\sum x_i^2}{N\sum x_i^2-(\sum x_i)^2}.\tag1
$$
Similarly the distribution of $\hat\beta_2$ is normal with mean $\beta_2$ and variance equal to the $(2,2)$ entry of the covariance matrix, which is
$$
\frac{\sigma^2}{N\sum x_i^2-(\sum x_i)^2}N=\sigma^2\frac{N}{N\sum x_i^2-(\sum x_i)^2}.\tag2
$$
In (1) and (2), the denominator can be rewritten $N\sum(x_i-\bar x)^2$.
If your model is
$$
Y_i = \theta x_i + \varepsilon_i,\qquad i=1,\ldots,N,
$$
then the design matrix $X$ is a column $[x_1, x_2,\ldots,x_N]'$ so $X'X=\sum x_i^2$ and the result (*) says that the estimator $\hat\theta$ is normal with mean $\theta$ and variance $\displaystyle\frac{\sigma^2}{\sum x_i^2}.$
A: $$
\widehat \beta_1 = \big[ 1, 0\big] \widehat\beta.
$$
Therefore
\begin{align}
& \operatorname E\left(\widehat\beta_1\right) = \big[1,0\big]\operatorname E \left( \widehat\beta \right) = \big[1,0\big]\beta = \beta_1. \\[10pt]
& \operatorname{var}\left(\widehat\beta_1\right) = \operatorname{var}\left( \big[1,0\big] \widehat\beta \right) = \big[1,0\big]\Big( \operatorname{var}\left(\widehat\beta\right) \Big) \left[ \begin{array}{c} 1 \\ 0 \end{array} \right] \\[10pt]
= {} & \sigma^2 \big[1,0\big] \left( \frac 1 \alpha
\begin{bmatrix} 
 \sum_{i=1}^N x_i^2 &  -\sum_{i=1}^N x_i \\
  &  \\
 -\sum_{i=1}^N x_i & N\\
\end{bmatrix} \right) \left[ \begin{array}{c} 1 \\ 0 \end{array} \right] = \cdots
\end{align}
That gives you the expected value and the variance. And if $\widehat\beta$ is bivariate normal then a linear combination of its components with constant (i.e. non-random) coefficients is univariate normal.
And similarly for $\widehat\beta_2.$
