In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSEij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), Fj (Partial F-test for the jth variable) and Fj(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSEIJ, FJ and FJ(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.Subset of variables, multiple linear regression, joint impact, regression diagnostics,

In Das [8], a study of slope rotatable designs with correlated error was initiated and second order slope-rotatability conditions over axial directions were derived for a general correlated error structure. In this article a class of multifactor designs for estimating the slope of a second order response surface regression model with correlated error is considered. Second order slope-rotatability over all directions and also with equal maximum directional variance in the case of two factors have been derived for a general correlated error structure. In the process, some measures have been proposed for robust slope-rotatability over axial directions, over all directions and with equal maximum directional variance, and are illustrated with examples.