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*******************************************************************************
Sun Oct 16 20:22:13 2022
FIT: data read from "ExpdataError.txt" us 1:2
format = x:z
#datapoints = 20
residuals are weighted equally (unit weight)
function used for fitting: f(x)
f(x)=a*exp(-b*x)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 9.6106450103e+00 0.00e+00 1.87e-01 1.000000e+00 1.000000e+00
7 2.5721145569e-02 -2.94e-03 1.87e+00 2.049217e+00 1.711930e-01
After 7 iterations the fit converged.
final sum of squares of residuals : 0.0257211
rel. change during last iteration : -2.93575e-08
degrees of freedom (FIT_NDF) : 18
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0378015
variance of residuals (reduced chisquare) = WSSR/ndf : 0.00142895
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 2.04922 +/- 0.02683 (1.309%)
b = 0.171193 +/- 0.003512 (2.051%)
correlation matrix of the fit parameters:
a b
a 1.000
b 0.651 1.000
*******************************************************************************
Sun Oct 16 20:26:40 2022
FIT: data read from "ExpdataError.txt" us 1:2:3 yerrors
format = x:z:s
#datapoints = 20
function used for fitting: f(x)
f(x)=a*exp(-b*x)
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 2.5734435745e+00 0.00e+00 7.14e+00 2.049217e+00 1.711930e-01
1 2.5734435581e+00 -6.36e-04 7.14e+00 2.049220e+00 1.711933e-01
After 1 iterations the fit converged.
final sum of squares of residuals : 2.57344
rel. change during last iteration : -6.36433e-09
degrees of freedom (FIT_NDF) : 18
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.378113
variance of residuals (reduced chisquare) = WSSR/ndf : 0.142969
p-value of the Chisq distribution (FIT_P) : 0.999992
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 2.04922 +/- 0.02683 (1.309%)
b = 0.171193 +/- 0.003512 (2.051%)
correlation matrix of the fit parameters:
a b
a 1.000
b 0.651 1.000