Table 2.

Quality of fits to kinetic constants. Sum of squares = 0.00058044

k (ms−1)	errors§	dependencies¶	τ = k−1 (ms)
238.5	1.6	1.1	0.00419
20.34	0.27	2.1	0.04917
7.338	0.13	2.2	0.1363
2.129	0.058	1.8	0.4697
0.4905	0.0030	1.6	2.039
0.2216	0.0014	2.6	4.512
0.0673	0.0054	2.4	14.85

^§Standard error in the estimate of each k. There is a 68% confidence level that the true value of k lies within ± the listed error.

^¶For a proper fit, the required number of parameters (i.e. exponential amplitudes and rate constants) must be provided. Too few parameters lead to large errors and non-random residuals. Too many cause errors of a different kind. The residuals become deceptively better, while the errors become quite large. The superfluous parameters typically provide two redundant exponentials to fit a single kinetic event, so that there is no unique solution for the two affected rate constants. Perturbations in the value of one of the extra parameters results in compensatory changes in the value of its partner(s). Therefore, when all of the parameters are fit simultaneously, the errors for the redundant constant(s) will be large. However, if all parameters but the redundant one are held constant, its error will be greatly reduced. The dependency value is defined as the ratio of the error for one fitted parameter when none of the other parameters is held constant, to the error when all others are held constant. Dependency values have a lower bound of 1, and no upper bound. Values below 10 indicate the absence of any significant redundancies in the parameters. Values above 10 indicate possible problems. Values above 50 provide a strong indication that too many parameters are being used to fit the data set.