This chapter describes routines for performing least squares fits to experimental data using linear combinations of functions. The data may be weighted or unweighted, i.e. with known or unknown errors. 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.
|• Fitting Overview:|
|• Linear regression:|
|• Multi-parameter regression:|
|• Regularized regression:|
|• Robust linear regression:|
|• Large Linear Systems:|
|• Fitting Examples for linear regression:|
|• Fitting Examples for multi-parameter linear regression:|
|• Fitting Examples for regularized linear regression:|
|• Fitting Examples for robust linear regression:|
|• Fitting Examples for large linear systems:|
|• Fitting References and Further Reading:|