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.

See,en.m.wikipedia.org/wiki/Nonlinear_regression

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

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

$$\ln{\hat y}=\beta\ln{\hat x}+\alpha$$

$$\ln{y}=\beta\ln{x}+\alpha+\epsilon~~........\epsilon~is~error$$

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.

See,en.m.wikipedia.org/wiki/Nonlinear_regression