This　paper　is　focused　on　the　mask　effects　between　diagnosis　of　outliers　and　of　response　transformation　in　regression　analysis.　Based　on　the　idea　of　variable　selection,　a　simultaneous　diagnosis　method　is　proposed　by　constructing　covariates　and　employing　the　genaralized　information　criterion.　The　efficiency　of　the　proposed　approach　is　compared　with　naive　methods　through　a　Monte　Carlo　simulation　and　two　examples.