Suppose we have vectors $x_1$ and $x_2$, each has ($n$) samples. Both $x_1$ and $x_2$ are my independent variables.
Suppose we also have a vector $y$ which has ($n$) samples and is y my dependent variable.
I would like to perform a linear regression in the form: $$y=b_0 + b_1 x_1 + b_2 x_2$$
however, before performing the regression, all my variables ($y,x_1,x_2$) were normalised such that their mean is zero and std. deviation is $1$.
After the regression, we get $a_0, a_1$ and $a_2$ which are the weights. How can I get the original weights ($b_0,b_1,b_2$) given the weights obtained from the normalised data ($a_0,a_1$ and $a_2$)?