Node:Least-Squares Fitting, Next:Nonlinear Least-Squares Fitting, Previous:Multidimensional Minimization, Up:Top

This chapter describes routines for performing least squares fits to
experimental data using linear combinations of functions. The data may
be weighted or unweighted. For weighted data the functions compute the
best fit parameters and their associated covariance matrix. For
unweighted data the covariance matrix is estimated from the scatter of
the points, giving a variance-covariance matrix. The functions are
divided into separate versions for simple one- or two-parameter
regression and multiple-parameter fits. The functions are declared in
the header file `gsl_fit.h`

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