Consider a Simple Linear Regression with the following assumptions:
The dependent variable is related to the independent variable and the error term like: $y = \beta_0 + \beta _1 x + u$
We have a random sample of size $n$ following the population model in assumption #1
The sample outcomes on $x$ are not all the same value
$E[u|x] = 0$ is true
I am reading on the derivation of the sample variance for $\beta _1$. However, I don't understand why they assume that $\Sigma _ {i=1} ^ n x_i - \bar x$ is treated like a constant. Why is this true?