Abstract

example. Regression analysis is a commonly used tool m the biological and physical sciences. In addition to fitting a regression model, further goals may include prediction, calibration, and estimation of detection limits. The data are often systematically heteroscedastic, with variance small relative to the range of the mean response, and possibly asymmetrically distributed. Transformation is frequently used to induce both constant variance and normality of the response, so that subsequent inference presumably can be undertaken in a standard manner. Recent approaches to data transformation and accomodation of heteroscedasticity with variance models are briefly reviewed in a discussion based on Carroll and Ruppert (1988). There are situations in which transformation can not remedy all of the nonstandard features of the data simultaneously. As an illustration, an assay data set is used to point out the relationship between transformation and weighted regression as it might occur in many applications, especially in biological and physical science.

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