 if the data is heteroscedastic), it may be possible to find a transformation of "Y" alone, or transformations of both "X" (the predictor variables) and "Y", such that the homoscedasticity assumption (in addition to the linearity assumption) holds true on the transformed variables and linear regression may therefore be applied on these.Yet another application of data transformation is to address the problem of lack of normality in error terms.Univariate normality is not needed for least squares estimates of the regression parameters to be meaningful (see Gauss–Markov theorem).