We begin the manuscript by describing our approach, including a description of the leastsquares fitting program, estimation of error, the introduction of the scale invariant gradient norm, a brief review of multicollinearity (or ill-conditioning) in fitting problems, preprocessing of data prior to fitting, and description of hardware and software used in measurements and least-squares fitting. We then examine several experiments including a side-by-side comparison of model fitting and the CLS method for 100 trials of simulated data, fitting with too many or too few Kubo components, fitting to low SNR data, fitting to data with phasing errors, fitting to experimental data, and fitting to undersampled data. We then conclude with a discussion of recommended practices for model fitting.