The evidence for transformation of the response in a regression model may sometimes depend crucially on one or a few observations. Diagnostic methods based on the deletion of single cases are well established. Multiple deletion methods are likewise well known, but are little applied because of combinatorial problems. But sometimes the pattern of multiple outliers and influential cases cannot be revealed by the sequential use of single deletion methods. In such instances. masking is said to occur. The method of unmasking used in this article is least-median-of-squares regression, calculated at several values of the transformation parameter. The structure of the residuals from this robust analysis serves as an exploratory method for the identification of outliers. The confirmatory least squares analysis uses multiple-deletion diagnostic methods. Addition diagnostics are also used. These are developed for the score test and estimated transformation parameter.