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Synthetic Langmuir-type adsorption data were analyzed by different methods of regression. The methods used were nonlinear regression, three linearized forms of the Langmuir equation, orthogonal distance regression, and neutral regression. In order to simulate experimental results, the simulated data were compromised with three different homoskedastic and heteroskedastic error distributions. The results show that the best method of regression depends on the error distributions in Ce and qe. If the error variance is homoskedastic, then orthogonal-distance regression would give the best estimates of isotherm parameters, followed by nonlinear regression. On the other hand, if the error is heteroskedastic (constant coefficient of variation), then one form of the linearized Langmuir equation would give the best estimates, followed by nonlinear regression. Weighted regression was found to improve the accuracy and precision of estimates in the case of heteroskedastic error. Another variable, which was ignored in previous research, was found to have a significant effect on the accuracy of estimates. This variable is the range of data as compared to the position of the Langmuir isotherm plateau. [ABSTRACT FROM AUTHOR]