Hi all,

I have a question about standardization.

Suppose I have training data which is a X matrix, of size N x p, where N is
the number of samples, p is the number of variables in the data set. Y is a
response vector of size N x 1, each element correspoding to each row of the
X matrix.

I do standardization on X, X1=scale(X, TRUE, TRUE), and Y1=scale(Y, TRUE,
FALSE).

And I got a regression coefficient vector Beta.

I am wondering how to I manipulate this Beta to run the test?

Is this Beta the same as the one we would obtain if we don't standization
the training data?

For testing data, I guess I should not standardize, otherwise, how can my
predicted data match with the non-standardized test Y vector?

If I do standardization on the test X matrix and Y vector, then I lose my
physical meaning of prediction error -- it will be distorted... since, at
the end of the day, I want to see the prediction accuracy in the origianl
non-standardized domain...

If I don't do standardization on the text X matrix and Y vector, I am not
sure if that Beta obtained through standardization is usable here...

I am confused by the fact that there is an extra intercept...  and there is
one additional coefficient called Beta0.

How does that intercept interact with my standardization and
not-standardization?

Thanks a lot!

	[[alternative HTML version deleted]]


