Fitting data set in Mathematica with many parameters and different datasets - NonLinearModelFit

Question

I'm new in Mathematica (first time using it) and I'm trying perform a fit, using NonLinearModelFit and I have some doubts and also I'm not sure that I'm doing in a correct way. So, to try explain easily my problem.

I have 24 files, each file with 15 lines. Then MyData is the first column when I do "allData" (1-24), the second column the time (0-14), the 3 column my data. Then I have 16 models, and in each model I have the variable "t" (changing to 1 to 24 - same number of files) that will change with the "Do". So for each t, I will read one of my data and use one of my files. In other words, I will perform the fit for the 16 models for each file (1-24).

(*reading my data*)
data= Import["/Users/gbailas/Documents/correlatorstofit/fit_version2/fort_1013.
txt", {"Data", {All}}]
(*I'm reading one by one and separating then)
data4 = data[[1 ;; 30, 1 ;; 2]]
(*Doing the same with my error)
error5 = data[[All, 3]]
(*I'm doing it for my 16 files)
allData = 
Join[{1, Sequence @@ #} & /@ data1, {2, Sequence @@ #} & /@ 
data2, {3, Sequence @@ #} & /@ data3, {4, Sequence @@ #} & /@ 
data4, {5, Sequence @@ #} & /@ data5, {6, Sequence @@ #} & /@ 
data6, {7, Sequence @@ #} & /@ data7, {8, Sequence @@ #} & /@ 
data8, {9, Sequence @@ #} & /@ data9, {10, Sequence @@ #} & /@ 
data10, {11, Sequence @@ #} & /@ data11, {12, Sequence @@ #} & /@ 
data12, {13, Sequence @@ #} & /@ data13, {14, Sequence @@ #} & /@ 
data14, {15, Sequence @@ #} & /@ data15, {16, Sequence @@ #} & /@ 
data16, {17, Sequence @@ #} & /@ data17, {18, Sequence @@ #} & /@ 
data18, {19, Sequence @@ #} & /@ data19, {20, Sequence @@ #} & /@ 
data20, {21, Sequence @@ #} & /@ data21, {22, Sequence @@ #} & /@ 
data22, {23, Sequence @@ #} & /@ data23, {24, Sequence @@ #} & /@ 
data24]
myF[index_] := 
KroneckerDelta[index - 1] model1 + KroneckerDelta[index - 2] model2 +
KroneckerDelta[index - 3] model3 + 
KroneckerDelta[index - 4] model4 + 
KroneckerDelta[index - 5] model5 + 
KroneckerDelta[index - 6] model6 + 
KroneckerDelta[index - 7] model7 + 
KroneckerDelta[index - 8] model8 + 
KroneckerDelta[index - 9] model9 + 
KroneckerDelta[index - 10] model10 + 
KroneckerDelta[index - 11] model11 + 
KroneckerDelta[index - 12] model12 + 
KroneckerDelta[index - 13] model13 + 
KroneckerDelta[index - 14] model14 + 
KroneckerDelta[index - 15] model15 + 
KroneckerDelta[index - 16] model16
(*Now, I'm writing my models to fit: I have 16 and they are similar)
   Do[
     model1 = 0.5*x11*11*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
0.5*x12*x12*(Exp[t*x2] + Exp[(64 - t)*x2]) + 0.5*x13*(Exp[t*x3] + Exp[(64 - t)*x3]);

model2 = 
0.5*x11*x21*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
0.5*x12*x22*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
0.5*x23*(Exp[t*x4] + Exp[(64 - t)*x4]);

 model3 = 
 0.5*x11*x31*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x12*x32*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x33*(Exp[t*x5] + Exp[(64 - t)*x5]);

 model4 = 
 0.5*x11*x41*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x12*x42*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x43*(Exp[t*x6] + Exp[(64 - t)*x6]); 

 model5 = model2;

 model6 = 
 0.5*x21*x21*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x22*x22*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x53*(Exp[t*x7] + Exp[(64 - t)*x7]);

 model7 = 
 0.5*x21*x31*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x22*x32*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x63*(Exp[t*x8] + Exp[(64 - t)*x8]);

 model8 = 
 0.5*x21*x41*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x22*x42*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x73*(Exp[t*x9] + Exp[(64 - t)*x9]);
 model9 = model3;
 model10 = model7;

 model11 = 
 0.5*x31*x31*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x32*x32*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x83*(Exp[t*x10] + Exp[(64 - t)*x10]);

 model12 = 
 0.5*x31*x41*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x32*x42*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x93*(Exp[t*x11b] + Exp[(64 - t)*x11b]);

 model13 = model4;
 model14 = model8;
 model15 = model12;

 model16 = 
 0.5*x41*x41*(Exp[t*x1] + Exp[(64 - t)*x1]) + 
 0.5*x42*x42*(Exp[t*x2] + Exp[(64 - t)*x2]) + 
 0.5*x103*(Exp[t*x12b] + Exp[(64 - t)*x12b]);   

 Print[fit = 
 NonlinearModelFit[allData, 
  myF[index], {{x11, 0.03}, {x12, 0.03}, {x13, 0.03}, {x21, 
  0.03}, {x22, 0.03}, {x23, 0.03}, {x31, 0.03}, {x32, 0.03}, {x33,
   0.03}, {x41, 0.03}, {x42, 0.03}, {x43, 0.03}, {x53, 
  0.03}, {x63, 0.03}, {x73, 0.03}, {x83, 0.03}, {x93, 
  0.03}, {x103, 0.03}, {x1, 0.98}, {x2, 
  1.2}, {x3}, {x4}, {x5}, {x6}, {x7}, {x8}, {x9}, {x10}, {x11b}, \
  {x12b}}, {index, x}, 
   Weights -> 
   Join[error1, error2, error3, error4, error5, error6, error7, 
   error8, error9, error10, error11, error12, error13, error14, 
   error15, error16, error17, error18, error19, error20, error21, 
   error22, error23, error24]]],
  {t, 24}]

nlm["BestFitParameters"]
nlm["ParameterConfidenceIntervalTable"]
nlm["CorrelationMatrix"]

My data:

    {{1, 0, 0.000383671}, {1, 1, 0.0000706219}, {1, 2, 0.00032182}, {1, 3,
    0.0000501746}, {1, 4, 0.0000705337}, {1, 5, 0.000016072}, {1, 6, 
   0.0000306593}, {1, 7, 6.23339*10^-6}, {1, 8, 0.000323322}, {1, 9, 
   0.0000282914}, {1, 10, 7.41876*10^-6}, {1, 11, 0.0000505202}, {1, 
   12, 5.73212*10^-6}, {1, 13, 7.97754*10^-6}, {1, 14, 
   8.55089*10^-7}, {2, 0, 0.000127432}, {2, 1, 0.0000256319}, {2, 2, 
   0.000100011}, {2, 3, 0.0000179721}, {2, 4, 0.0000256286}, {2, 5, 
   5.92758*10^-6}, {2, 6, 0.000010643}, {2, 7, 2.25945*10^-6}, {2, 8, 
   0.000100443}, {2, 9, 9.81189*10^-6}, {2, 10, 0.0000264367}, {2, 11, 
   2.55395*10^-6}, {2, 12, 0.0000180715}, {2, 13, 2.0765*10^-6}, {2, 
   14, 2.74392*10^-6}, {3, 0, 0.0000439533}, {3, 1, 9.36508*10^-6}, {3,
   2, 0.000032982}, {3, 3, 6.5074*10^-6}, {3, 4, 9.36169*10^-6}, {3, 
   5, 2.19089*10^-6}, {3, 6, 3.76078*10^-6}, {3, 7, 8.24936*10^-7}, {3,
   8, 0.0000331415}, {3, 9, 3.46065*10^-6}, {3, 10, 
   8.65011*10^-6}, {3, 11, 8.95256*10^-7}, {3, 12, 6.54521*10^-6}, {3, 
   13, 7.58665*10^-7}, {3, 14, 9.62327*10^-7}, {4, 0, 
   0.0000155284}, {4, 1, 3.43991*10^-6}, {4, 2, 0.0000113013}, {4, 3, 
   2.36799*10^-6}, {4, 4, 3.43626*10^-6}, {4, 5, 8.12941*10^-7}, {4, 6,
   1.34602*10^-6}, {4, 7, 3.02481*10^-7}, {4, 8, 0.0000113686}, {4, 9,
   1.23722*10^-6}, {4, 10, 2.93995*10^-6}, {4, 11, 3.17998*10^-7}, {4,
   12, 2.38758*10^-6}, {4, 13, 2.78251*10^-7}, {4, 14, 
   3.4201*10^-7}, {5, 0, 5.5762*10^-6}, {5, 1, 1.2684*10^-6}, {5, 2, 
   3.96688*10^-6}, {5, 3, 8.68586*10^-7}, {5, 4, 1.26848*10^-6}, {5, 5,
   3.02467*10^-7}, {5, 6, 4.86171*10^-7}, {5, 7, 1.11531*10^-7}, {5, 
   8, 3.99318*10^-6}, {5, 9, 4.47042*10^-7}, {5, 10, 
   1.02641*10^-6}, {5, 11, 1.14352*10^-7}, {5, 12, 8.75988*10^-7}, {5, 
   13, 1.02661*10^-7}, {5, 14, 1.2308*10^-7}, {6, 0, 
   2.02352*10^-6}, {6, 1, 4.69155*10^-7}, {6, 2, 1.41666*10^-6}, {6, 3,
   3.19562*10^-7}, {6, 4, 4.68886*10^-7}, {6, 5, 1.12495*10^-7}, {6, 
   6, 1.76912*10^-7}, {6, 7, 4.1183*10^-8}, {6, 8, 1.42561*10^-6}, {6, 
   9, 1.62676*10^-7}, {6, 10, 3.64851*10^-7}, {6, 11, 
   4.13672*10^-8}, {6, 12, 3.22381*10^-7}, {6, 13, 3.79496*10^-8}, {6, 
   14, 4.46784*10^-8}, {7, 0, 7.40178*10^-7}, {7, 1, 
   1.73899*10^-7}, {7, 2, 5.12031*10^-7}, {7, 3, 1.17975*10^-7}, {7, 4,
   1.73924*10^-7}, {7, 5, 4.18974*10^-8}, {7, 6, 6.47739*10^-8}, {7, 
   7, 1.52509*10^-8}, {7, 8, 5.15461*10^-7}, {7, 9, 5.95719*10^-8}, {7,
   10, 1.31406*10^-7}, {7, 11, 1.51048*10^-8}, {7, 12, 
   1.19095*10^-7}, {7, 13, 1.40664*10^-8}, {7, 14, 1.63467*10^-8}, {8, 
   0, 2.72305*10^-7}, {8, 1, 6.45798*10^-8}, {8, 2, 1.86712*10^-7}, {8,
   3, 4.37123*10^-8}, {8, 4, 6.46247*10^-8}, {8, 5, 
   1.56087*10^-8}, {8, 6, 2.38353*10^-8}, {8, 7, 5.66359*10^-9}, {8, 8,
   1.87744*10^-7}, {8, 9, 2.18757*10^-8}, {8, 10, 4.77339*10^-8}, {8, 
  11, 5.55168*10^-9}, {8, 12, 4.41001*10^-8}, {8, 13, 
  5.21541*10^-9}, {8, 14, 6.00597*10^-9}, {9, 0, 1.00577*10^-7}, {9, 
  1, 2.40455*10^-8}, {9, 2, 6.85328*10^-8}, {9, 3, 1.62522*10^-8}, {9,
  4, 2.40433*10^-8}, {9, 5, 5.83217*10^-9}, {9, 6, 
 8.80243*10^-9}, {9, 7, 2.10974*10^-9}, {9, 8, 6.89136*10^-8}, {9, 9,
  8.09048*10^-9}, {9, 10, 1.74927*10^-8}, {9, 11, 2.04672*10^-9}, {9,
  12, 1.63687*10^-8}, {9, 13, 1.94204*10^-9}, {9, 14, 
  2.22003*10^-9}, {10, 0, 3.72676*10^-8}, {10, 1, 8.95541*10^-9}, {10,
   2, 2.52647*10^-8}, {10, 3, 6.04506*10^-9}, {10, 4, 
   8.95768*10^-9}, {10, 5, 2.17615*10^-9}, {10, 6, 3.25928*10^-9}, {10,
   7, 7.85561*10^-10}, {10, 8, 2.53829*10^-8}, {10, 9, 
   2.99893*10^-9}, {10, 10, 6.42558*10^-9}, {10, 11, 
  7.56666*10^-10}, {10, 12, 6.08207*10^-9}, {10, 13, 
  7.24365*10^-10}, {10, 14, 8.21026*10^-10}, {11, 0, 
  1.38419*10^-8}, {11, 1, 3.34183*10^-9}, {11, 2, 9.34098*10^-9}, {11,
  3, 2.24476*10^-9}, {11, 4, 3.33914*10^-9}, {11, 5, 
  8.12751*10^-10}, {11, 6, 1.2087*10^-9}, {11, 7, 
  2.92251*10^-10}, {11, 8, 9.39595*10^-9}, {11, 9, 
  1.11212*10^-9}, {11, 10, 2.37496*10^-9}, {11, 11, 
  2.8132*10^-10}, {11, 12, 2.26579*10^-9}, {11, 13, 
  2.6946*10^-10}, {11, 14, 3.04403*10^-10}, {12, 0, 
  5.14901*10^-9}, {12, 1, 1.247*10^-9}, {12, 2, 3.461*10^-9}, {12, 3, 
  8.36024*10^-10}, {12, 4, 1.24652*10^-9}, {12, 5, 
  3.03828*10^-10}, {12, 6, 4.49178*10^-10}, {12, 7, 
  1.08987*10^-10}, {12, 8, 3.47799*10^-9}, {12, 9, 
  4.13095*10^-10}, {12, 10, 8.78796*10^-10}, {12, 11, 
  1.04439*10^-10}, {12, 12, 8.42875*10^-10}, {12, 13, 
  1.0053*10^-10}, {12, 14, 1.12985*10^-10}, {13, 0, 
  1.9168*10^-9}, {13, 1, 4.65382*10^-10}, {13, 2, 1.28573*10^-9}, {13,
   3, 3.11567*10^-10}, {13, 4, 4.65198*10^-10}, {13, 5, 
  1.13574*10^-10}, {13, 6, 1.67166*10^-10}, {13, 7, 
  4.06472*10^-11}, {13, 8, 1.29171*10^-9}, {13, 9, 
  1.5351*10^-10}, {13, 10, 3.26685*10^-10}, {13, 11, 
  3.87996*10^-11}, {13, 12, 3.14184*10^-10}, {13, 13, 
  3.74167*10^-11}, {13, 14, 4.21464*10^-11}, {14, 0, 
  7.14198*10^-10}, {14, 1, 1.73651*10^-10}, {14, 2, 
  4.7871*10^-10}, {14, 3, 1.16543*10^-10}, {14, 4, 
  1.73648*10^-10}, {14, 5, 4.24223*10^-11}, {14, 6, 
  6.23357*10^-11}, {14, 7, 1.52245*10^-11}, {14, 8, 
  4.7958*10^-10}, {14, 9, 5.7062*10^-11}, {14, 10, 
  1.21766*10^-10}, {14, 11, 1.44665*10^-11}, {14, 12, 
  1.17051*10^-10}, {14, 13, 1.39577*10^-11}, {14, 14, 
  1.57245*10^-11}, {15, 0, 2.66262*10^-10}, {15, 1, 
  6.48156*10^-11}, {15, 2, 1.78493*10^-10}, {15, 3, 
  4.3535*10^-11}, {15, 4, 6.48186*10^-11}, {15, 5, 
  1.58306*10^-11}, {15, 6, 2.32703*10^-11}, {15, 7, 
  5.68821*10^-12}, {15, 8, 1.79102*10^-10}, {15, 9, 
   2.13051*10^-11}, {15, 10, 4.54559*10^-11}, {15, 11, 
  5.40509*10^-12}, {15, 12, 4.37823*10^-11}, {15, 13, 
  5.21523*10^-12}, {15, 14, 5.86822*10^-12}, {16, 0, 
  9.93564*10^-11}, {16, 1, 2.42161*10^-11}, {16, 2, 
  6.6572*10^-11}, {16, 3, 1.62293*10^-11}, {16, 4, 
  2.42239*10^-11}, {16, 5, 5.91841*10^-12}, {16, 6, 
  8.68707*10^-12}, {16, 7, 2.12454*10^-12}, {16, 8, 
  6.67592*10^-11}, {16, 9, 7.94842*10^-12}, {16, 10, 
  1.69107*10^-11}, {16, 11, 2.01434*10^-12}, {16, 12, 
  1.63438*10^-11}, {16, 13, 1.94791*10^-12}, {16, 14, 
  2.1877*10^-12}, {17, 0, 3.70976*10^-11}, {17, 1, 
  9.05175*10^-12}, {17, 2, 2.48654*10^-11}, {17, 3, 
  6.07012*10^-12}, {17, 4, 9.05976*10^-12}, {17, 5, 
 2.21581*10^-12}, {17, 6, 3.24892*10^-12}, {17, 7, 
  7.95438*10^-13}, {17, 8, 2.4954*10^-11}, {17, 9, 
 2.97675*10^-12}, {17, 10, 6.31831*10^-12}, {17, 11, 
 7.52379*10^-13}, {17, 12, 6.11849*10^-12}, {17, 13, 
 7.31358*10^-13}, {17, 14, 8.16784*10^-13}, {18, 0, 
 1.38582*10^-11}, {18, 1, 3.3849*10^-12}, {18, 2, 
 9.28409*10^-12}, {18, 3, 2.26626*10^-12}, {18, 4, 
 3.38852*10^-12}, {18, 5, 8.28689*10^-13}, {18, 6, 
 1.21432*10^-12}, {18, 7, 2.96867*10^-13}, {18, 8, 
 9.34011*10^-12}, {18, 9, 1.1125*10^-12}, {18, 10, 
 2.35919*10^-12}, {18, 11, 2.81116*10^-13}, {18, 12, 
 2.29451*10^-12}, {18, 13, 2.73739*10^-13}, {18, 14, 
 3.04584*10^-13}, {19, 0, 5.17704*10^-12}, {19, 1, 
 1.26442*10^-12}, {19, 2, 3.4657*10^-12}, {19, 3, 8.474*10^-13}, {19,
 4, 1.26592*10^-12}, {19, 5, 3.09502*10^-13}, {19, 6, 
 4.53599*10^-13}, {19, 7, 1.11042*10^-13}, {19, 8, 
 3.49011*10^-12}, {19, 9, 4.15734*10^-13}, {19, 10, 
 8.7906*10^-13}, {19, 11, 1.04643*10^-13}, {19, 12, 
 8.58117*10^-13}, {19, 13, 1.02341*10^-13}, {19, 14, 
 1.13606*10^-13}, {20, 0, 1.93368*10^-12}, {20, 1, 
 4.7277*10^-13}, {20, 2, 1.29376*10^-12}, {20, 3, 
 3.1718*10^-13}, {20, 4, 4.72985*10^-13}, {20, 5, 
 1.15854*10^-13}, {20, 6, 1.69364*10^-13}, {20, 7, 
 4.15353*10^-14}, {20, 8, 1.30362*10^-12}, {20, 9, 
 1.55619*10^-13}, {20, 10, 3.28251*10^-13}, {20, 11, 
 3.92229*10^-14}, {20, 12, 3.20549*10^-13}, {20, 13, 
 3.82868*10^-14}, {20, 14, 4.24187*10^-14}, {21, 0, 
 7.22578*10^-13}, {21, 1, 1.76822*10^-13}, {21, 2, 
 4.83091*10^-13}, {21, 3, 1.18379*10^-13}, {21, 4, 
 1.76833*10^-13}, {21, 5, 4.33384*10^-14}, {21, 6, 
 6.32304*10^-14}, {21, 7, 1.55028*10^-14}, {21, 8, 
 4.87369*10^-13}, {21, 9, 5.81692*10^-14}, {21, 10, 
 1.22859*10^-13}, {21, 11, 1.46754*10^-14}, {21, 12, 
 1.19753*10^-13}, {21, 13, 1.43013*10^-14}, {21, 14, 
 1.58599*10^-14}, {22, 0, 2.69953*10^-13}, {22, 1, 
 6.61098*10^-14}, {22, 2, 1.80389*10^-13}, {22, 3, 
 4.42085*10^-14}, {22, 4, 6.60753*10^-14}, {22, 5, 
 1.62041*10^-14}, {22, 6, 2.36236*10^-14}, {22, 7, 
 5.79979*10^-15}, {22, 8, 1.82287*10^-13}, {22, 9, 
 2.17767*10^-14}, {22, 10, 4.5868*10^-14}, {22, 11, 
 5.48248*10^-15}, {22, 12, 4.4744*10^-14}, {22, 13, 
 5.34856*10^-15}, {22, 14, 5.92304*10^-15}, {23, 0, 
 1.00925*10^-13}, {23, 1, 2.47257*10^-14}, {23, 2, 
 6.74046*10^-14}, {23, 3, 1.65415*10^-14}, {23, 4, 
 2.47289*10^-14}, {23, 5, 6.06419*10^-15}, {23, 6, 
 8.83337*10^-15}, {23, 7, 2.17051*10^-15}, {23, 8, 
 6.81016*10^-14}, {23, 9, 8.14167*10^-15}, {23, 10, 
 1.72029*10^-14}, {23, 11, 2.05734*10^-15}, {23, 12, 
 1.67284*10^-14}, {23, 13, 2.00005*10^-15}, {23, 14, 
 2.21597*10^-15}, {24, 0, 3.77596*10^-14}, {24, 1, 
 9.25321*10^-15}, {24, 2, 2.5193*10^-14}, {24, 3, 
 6.19405*10^-15}, {24, 4, 9.25753*10^-15}, {24, 5, 
 2.26901*10^-15}, {24, 6, 3.30317*10^-15}, {24, 7, 
 8.12531*10^-16}, {24, 8, 2.54431*10^-14}, {24, 9, 
 3.03733*10^-15}, {24, 10, 6.45621*10^-15}, {24, 11, 
 7.72003*10^-16}, {24, 12, 6.24942*10^-15}, {24, 13, 
 7.45508*10^-16}, {24, 14, 8.30793*10^-16}}

Output

 {
 {"", "Estimate", "Standard Error", "Confidence Interval"},
 {x11, -0.0785915, 2.9389*10^-17, {-0.0785915, -0.0785915}},
 {x12, 0.0270872, 2.22768*10^-16, {0.0270872, 0.0270872}},
 {x13, -0.258502, 3.23456*10^-17, {-0.258502, -0.258502}},
 {x21, -2.62527, 2.99177*10^-17, {-2.62527, -2.62527}},
 {x22, 0.0318795, 5.22249*10^-16, {0.0318795, 0.0318795}},
 {x23, -0.174377, 7.64089*10^-17, {-0.174377, -0.174377}},
 {x31, 1.25575, 1.45992*10^-17, {1.25575, 1.25575}},
 {x32, 0.0164087, 2.05095*10^-16, {0.0164087, 0.0164087}},
 {x33, -0.241841, 2.53*10^-17, {-0.241841, -0.241841}},
 {x41, -1.92292, 6.94623*10^-17, {-1.92292, -1.92292}},
 {x42, 0.0184719, 8.99529*10^-16, {0.0184719, 0.0184719}},
 {x43, -0.194541, 1.41065*10^-16, {-0.194541, -0.194541}},
 {x53, -0.131034, 1.80937*10^-16, {-0.131034, -0.131034}},
 {x63, -0.0177579, 8.0498*10^-16, {-0.0177579, -0.0177579}},
 {x73, -0.100532, 2.29897*10^-16, {-0.100532, -0.100532}},
 {x83, -0.465108, 3.0238*10^-17, {-0.465108, -0.465108}},
 {x93, -0.657169, 2.3635*10^-18, {-0.657169, -0.657169}},
 {x103, -1.24714, 6.88846*10^-17, {-1.24714, -1.24714}},
 {x1, 0.965638, 1.0429*10^-15, {0.965638, 0.965638}},
 {x2, 1.19089, 2.31676*10^-16, {1.19089, 1.19089}},
 {x3, 1.04039, 3.34455*10^-16, {1.04039, 1.04039}},
 {x4, 1.05891, 5.32957*10^-16, {1.05891, 1.05891}},
 {x5, 1.03273, 2.44744*10^-16, {1.03273, 1.03273}},
 {x6, 1.04272, 1.09771*10^-15, {1.04272, 1.04272}},
 {x7, 1.0845, 9.48356*10^-16, {1.0845, 1.0845}},
 {x8, 1.06604, 5.71789*10^-16, {1.06604, 1.06604}},
 {x9, 1.0803, 9.24482*10^-16, {1.0803, 1.0803}},
 {x10, 1.01802, 5.62558*10^-16, {1.01802, 1.01802}},
 {x11b, 0.908343, 6.21287*10^-17, {0.908343, 0.908343}},
 {x12b, 1.00688, 3.43634*10^-15, {1.00688, 1.00688}}

Then, I'm not sure that this fit is correct, because for me the errors are too small, even when I don't consider the weights my errors are like this. So, maybe there is something wrong in this fit. Do you think I'm doing in the correct way?

I accept all advices and suggestions. I have tried to use all my information here to explain my problem.

Maybe this is not a good way to perform this fit, but since is my first time using Mathematica and I'm doing it alone, I'm trying in my way.

Thanks for help me. Best wishes, Gabriela