Abstract
Ordinary least squares (OLS) is one of the most commonly used criteria for fitting data to models and for estimating parameters. Orthogonal distance regression (ODR) extends least squares data fitting to problems with independent variables that are not known exactly. In this paper, we present the results of an empirical study designed to compare OLS to ODR for fitting both linear and non-linear models when there are errors in the independent variables. The results indicate that, for the data and performance criteria considered, ODR never performs appreciably worse than OLS and often performs considerably better.
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