On Effects of Augmentation of data on Condition Index

There are three approaches in the literature to remedy the multicollinearity in design matrix in a linear regression: augmentation of data, variable selection and alternative procedures to the ordinary least squares. In this note our emphasis is focused on the effect of augmentation of data on condition index. Our results show that when the additional data are properly chosen in practical possible situation, the condition index of design matrix can be reduced. the results obtained here are illustrated by Gaylor-Merrill data 1 which has been extensively discussed in the literature on regression optimal design.