  Preprocessing data is an important step before any data analysis. In this
paper, we focus on one particular aspect, namely scaling or normalization. We
analyze various scaling methods in common use and study their effects on
different statistical learning models. We will propose a new two-stage scaling
method. First, we use some training data to fit linear regression model and
then scale the whole data based on the coefficients of regression. Simulations
are conducted to illustrate the advantages of our new scaling method. Some real
data analysis will also be given.
