Fitting data to nonlinear functions with uncertainties in all measurement variables

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Abstract
An analytical approach is made to the nonlinear least squares problem having uncertainties in all measurements coordinates. It is shown how the unknown independent coordinates may be eliminated from the sum of the squares, thus reducing it to an ordinary minimisation problem. An algorithm is derived using analytical derivatives, but a simple procedure also allows the use of standard numerical derivatives.

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