Linear regression scaling independent variables I am trying to do a linear regression.
My $y$ variable is typical pretty small approx 0 to 0.3
I have some $x$ variables (regressing them individually on $y$ to start with) though that are very large. I have some $x$ variables that are 30,000. I have been told I should rebase these $x$ variables. 
My question is will this make any difference? Also what is the best way to rebase these $x$ variables assuming a linear relationship?
 A: Values of independent variables $x$ are very high compare to dependent $y$, $\beta$-value would be very small, there will be many errors like.......sampling error, rounding error.
Yes! you really need to rebase $x$, The best way is to take logs
$$\ln{\hat y}=\beta\ln{\hat x}+\alpha$$
$$\ln{y}=\beta\ln{x}+\alpha+\epsilon~~........\epsilon~is~error$$
Note:- the linear form is linear in the regression parameters associated with the covariates.
Nonlinear regression
A: Using logs of the inputs will make your model nonlinear but that may be what you want. With financial data, natural log usually makes models more predictable.
Typically if you what to have the coefficients have the same scale, you normalize the inputs by: x=(x-mean(x))/stdev(x)
That being said, it actually doesn't really matter if you don't rescale the inputs as long as the software displays all of the digits. However, you won't be able to see the influence of the particular input by the coefficient magnitude due to the differing scales.
