I'm new in statistical modelling and using R, so please excuse my mistake for this question.

I want to make multiple regression model with these variables:

- Revenue (in million USD) as dependent variable
- Customer experience score (with scale 1 to 5) as independent variable
- Number of package return (in unit) as independent variable

Since they have different unit and the variation is quite big, I'm thinking about standardize the variables before perform the regression. **Is it will be better to model with standardize variable or do regression directly?** I also read from the following source about how to rescale it with R.

But **how to interpret the model if the variables are rescaled and no longer has a certain unit?**

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#### Best Answer

Is it will be better to model with standardize variable or do regression directly?

In this case it makes no difference, and the latter (doing regression directly) is probably better, insofar as it is simpler. If you perform a regression directly then you have the advantage that the coefficient estimates will refer to estimated slopes relating the variables on the scale of the units you are using. If you standardise the variables first then the only effect will be to alter the coefficients onto a different scale that measures the slopes relating the *standardised* variables. It is possible to transition between one form and the other by simple algebra, but the latter form is probably less useful for you.

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