1

          LEAST SQUARES PARAMETER ESTIMATION - Marquardt method


               DATA from file: Szekeres Ákos  1.rend lineáris         



               Number of PARAMETERS............. 2
               Number of FUNCTIONS.............. 1
               Number of independent VARIABLES.. 1
               Number of DATA POINTS............ 15
               Convergency limit................ 1.0E-15
                 Equal weights

               Starting parameters:

  -2.000E-01 1.000E-01




                              D A T A   P O I N T S

  ---------------------------------------------------------------------------
    X1    Y1
  ---------------------------------------------------------------------------
    1    1.0000     .0402
    2    2.0000    -.2588
    3    3.0000    -.4510
    4    4.0000    -.5816
    5    5.0000    -.6872

    6    6.0000    -.7897
    7    7.0000    -.8867
    8    8.0000    -.9545
    9    9.0000    -.9835
   10   10.0000   -1.0189

   11   11.0000   -1.0498
   12   12.0000   -1.1117
   13   13.0000   -1.1520
   14   14.0000   -1.2140
   15   15.0000   -1.2588

  ---------------------------------------------------------------------------
1


                     Szekeres Ákos  1.rend lineáris         



          Estimates of the PARAMETERS     of their STANDARD DEVIATIONS


       1                -.8238666737        2.105E-01

       2                 .0000000009        2.269E-02




                         95 % confidence intervals

               half-width        lower limit - upper limit

        P(1)     4.538E-01       -1.278E+00  -  -3.700E-01

        P(2)     4.892E-02       -4.892E-02  -   4.892E-02





     Principal component analysis of correlation matrix


     EIGENVALUE    P(1)    P(2)

        1.880 .707     .000

         .120-.707     .707




          Desired convergence not obtained
1


          Fit of the model at the actual data points



                    MEAN=**********        VARIANCE= 1.394E-01

                RESIDUAL= 1.626E-01             FIT=   .000%



                    Number of iterations............ 51
                    Sum of squared residuals........1.950950E+00
                    Chi2 (if proper weights used)...*******
                    Reduced chi2 (if pr. weights)...*******
                    Number of degrees of freedom.... 13
                    Estimated weighted error........3.874E-01
                    Critical t (95 % confidence)....2.156
                    Goodness of overall fit.........   .000%

             Calculated values of the model function and residuals
                       are listed in the file FIT.OUT



               Correlation matrix of the parameters


  P(1)  P(2)

     P(1)    1.000

     P(2)     .880    1.000