Abstract
Non-linear least absolute deviation fitting of data has been shown to be superior to least squares where the data errors are unevenly distributed about the function. The methods give insignificantly different results for evenly-distributed errors. Criteria are given for choosing between least absolute deviation and least squares error minimization. Optical density measurements have non-Gaussian error distributions and are better fitted with minimization of the absolute deviation.
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