ABSTRACT
One of the most common problems in advanced applied statistics is the study of the relation between a single continuous response variatle and a number of explanatory variables. When the expected response can be represented as a linear combination of unknown parameters, with coefficients determined by the explanatory variables, and when the error structure is suitably simple, the techniques of multiple regression based on the method of least squares are applicable. A special form of correlated error structure is that of clustering of individuals into groups, the regression relations between and within groups being quite different. The residual from the fitted model is an index for each power station assessing its cost relative to what might have been anticipated given the explanatory variable. The relevant regression coefficient predicts that change, provided that the other explanatory variables are held fixed and that any important unobserved explanatory variables change appropriately with the change in size.
