Editing Nonlinear regression (section)

==Ordinary and weighted least squares==

The best-fit curve is often assumed to be that which minimizes the sum of squared [[errors and residuals in statistics|residuals]]. This is the [[ordinary least squares]] (OLS) approach.  However, in cases where the dependent variable does not have constant variance, or there are some outliers, a sum of weighted squared residuals may be minimized; see [[weighted least squares]]. Each weight should ideally be equal to the reciprocal of the variance of the observation, or the reciprocal of the dependent variable to some power in the outlier case,<ref>{{cite journal | last1 = Motulsky | first1 = H.J. | last2 = Ransnas | first2 = L.A. | title = Fitting curves to data using nonlinear regression: a practical and nonmathematical review | journal = The FASEB Journal | volume = 1 | issue = 5 | pages = 365–374 | year = 1987 | doi = 10.1096/fasebj.1.5.3315805 | doi-access = free | pmid = 3315805 }}</ref> but weights may be recomputed on each iteration, in an iteratively weighted least squares algorithm.