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:<math> f(x_i,\boldsymbol\beta) \approx f(x_i,0) + \sum_j J_{ij} \beta_j </math>
where <math>J_{ij} = \frac{\partial f(x_i,\boldsymbol\beta)}{\partial \beta_j}</math> are Jacobian matrix elements. It follows from this that the least squares estimators are given by
:<math>\hat{\boldsymbol{\beta}} \approx \mathbf { (J^TJ)^{-1}J^Ty},</math>
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