Simple regression analysis is based on the least square of residual errors. To use this method, an independent variable which is observable without error is assumed to exist. For most analyses, the assumption is normally made without realising the implications. The present study demonstrates how parameters may be estimated with the assumption of error in all measurements. Data sets involving batch/fed-batch cultivation of Candida utilis were analysed using a statistical approach with error in variables for the estimation of true biomass energetic yield and maintenance coefficient. This approach requires replicated measurements. For linear systems, such as in chemical/biochemical processes, replicated measurements are sometimes possible or there are physical/physiological reasons to group the observations into clusters. When clusters of replicated measurements are not available, the cluster analysis technique may be used to identify groups of near-replicated data points. The procedure yielded similar results to those obtained by the ordinary least squares procedure for consistent data. However, for data of poor quality (especially data with large errors in the independent variable), the disparity between the two procedures was more significant.