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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

Correlation Matrix

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  • $\begingroup$ Welcome to Mathematica.SE! I suggest the following: 1) As you receive help, try to give it too, by answering questions in your area of expertise. 2) Take the tour! 3) When you see good questions and answers, vote them up by clicking the gray triangles, because the credibility of the system is based on the reputation gained by users sharing their knowledge. Also, please remember to accept the answer, if any, that solves your problem, by clicking the checkmark sign! $\endgroup$ – Michael E2 Oct 5 '17 at 15:04
  • $\begingroup$ Was the output and fit reasonable? You don't mention the output or any error messages. $\endgroup$ – JimB Oct 5 '17 at 15:44
  • $\begingroup$ Have you seen this, this, this or this? $\endgroup$ – aardvark2012 Oct 5 '17 at 22:42
  • $\begingroup$ @aardvark2012 I've seen all of this. $\endgroup$ – Gabriela Oct 6 '17 at 10:48
  • $\begingroup$ @JimB There is no error message, Mathematica is finding some answers, but they are not completely correct for my case. What I need to do is a loop to make a fit for all t=0, then t=1 and so on. $\endgroup$ – Gabriela Oct 6 '17 at 10:48

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