Least square fitting with one explicit parameter less

Published: 1 March 2016| Version 1 | DOI: 10.17632/756m9mk65z.1
Bernd A. Berg


Abstract It is shown that whenever the multiplicative normalization of a fitting function is not known, least square fitting by ^(χ2)minimization can be performed with one parameter less than usual by converting the normalization parameter into a function of the remaining parameters and the data. Title of program: FITM1 Catalogue Id: AEYG_v1_0 Nature of problem Least square minimization when one of the free parameters is the multiplicative normalization of the fitting function. Versions of this program held in the CPC repository in Mendeley Data AEYG_v1_0; FITM1; 10.1016/j.cpc.2015.09.021 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)



Computational Physics, Computational Method