I understand the concept of scaling the data matrix to use in a linear regression model. For example, in R you could use:
scaled.data <- scale(data, scale=TRUE)
My only question is, for new observations for which I want to predict the output values, how are they correctly scaled? Would it be,
scaled.new <- (new - mean(data)) / std(data)?