I am becoming familiar with different statistical models starting with linear regression (simple and multiple). I understand that many statistical models are sensitive to scaling (those that are distance based).
There are different types of scaling (standardization, centering, normalization, etc.). Scaling can certainly help with visualizations if one variable has a range that is way larger than the other (ex: scatterplot).
My question: does linear regression (simple or multiple) work better if the explanatory variables X and the response variable Y are first scaled? Or is scaling only necessary if the ranges of the X and Y variables are VERY different? And what type of scaling would be the most appropriate?
I understand there is not a single scaling solution but I wonder what is the best way to think and approach scaling...