I am reading Simple Regression Model from this book, Section 6.5 (page 267 in downloaded pdf, 276 if viewed online).
The author starts with below equation for a simple linear regression model,
$$ Y_i = \alpha_1 + \beta x_i + \varepsilon_i $$
And then after few lines, he lets for conveience
that, $\alpha_1 = \alpha - \beta\overline{x}$ so that,
$$ Y_i = \alpha + \beta(x_i - \overline{x}) + \varepsilon_i $$
where $\overline{x} = \dfrac{1}{n}\sum\limits_{i=1}^nx_i$
My questions:
1. It is not convincing to bring in $\overline{x}$ just for convenience sake in the equation. Can any one please explain the logic behind bring that in the equation?
2. After above equation, the author says, $Y_i$ is equal to a nonrandom quantity, $\alpha + \beta(x_i - \overline{x})$, plus a mean zero normal random variable $\varepsilon_i$. Does that mean, $\alpha + \beta(x_i - \overline{x})$ has no randomness involved in that?
Kindly help.