Scaling variables in regression

If you need to scale variables in order to get the correct effect size and beta weight for each variable, does this mean you also scale for both categorical and continuous variables?

For example:
Model1=lm(DV ~ X + Y + Z)

In the above equation, is it correct to scale for x/y/z or would you only scale for x/y (assuming z is the only categorical variable)?

What should this look like in the R program?
Ultimately I want to be able to interpret what the coefficients mean, and I thought you had to standardize to make sure all variables are of the same range in order to do so.


Less is more. Stay pure. Stay poor.
You don't have to standardize to interpret coefficients. However, some people standardize to compare coefficients and their effects. So if you wanted to and it made sense given your context, you could standardize all of the variable to try and rank them.