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      1 NIST/ITL StRD
      2 Dataset Name:  MGH17             (MGH17.dat)
      3 
      4 File Format:   ASCII
      5                Starting Values   (lines 41 to 45)
      6                Certified Values  (lines 41 to 50)
      7                Data              (lines 61 to 93)
      8 
      9 Procedure:     Nonlinear Least Squares Regression
     10 
     11 Description:   This problem was found to be difficult for some very
     12                good algorithms.
     13 
     14                See More, J. J., Garbow, B. S., and Hillstrom, K. E.
     15                (1981).  Testing unconstrained optimization software.
     16                ACM Transactions on Mathematical Software. 7(1):
     17                pp. 17-41.
     18 
     19 Reference:     Osborne, M. R. (1972).  
     20                Some aspects of nonlinear least squares 
     21                calculations.  In Numerical Methods for Nonlinear 
     22                Optimization, Lootsma (Ed).  
     23                New York, NY:  Academic Press, pp. 171-189.
     24  
     25 Data:          1 Response  (y)
     26                1 Predictor (x)
     27                33 Observations
     28                Average Level of Difficulty
     29                Generated Data
     30 
     31 Model:         Exponential Class
     32                5 Parameters (b1 to b5)
     33 
     34                y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5]  +  e
     35 
     36 
     37 
     38           Starting values                  Certified Values
     39 
     40         Start 1     Start 2           Parameter     Standard Deviation
     41   b1 =     50         0.5          3.7541005211E-01  2.0723153551E-03
     42   b2 =    150         1.5          1.9358469127E+00  2.2031669222E-01
     43   b3 =   -100        -1           -1.4646871366E+00  2.2175707739E-01
     44   b4 =      1          0.01        1.2867534640E-02  4.4861358114E-04
     45   b5 =      2          0.02        2.2122699662E-02  8.9471996575E-04
     46 
     47 Residual Sum of Squares:                    5.4648946975E-05
     48 Residual Standard Deviation:                1.3970497866E-03
     49 Degrees of Freedom:                                28
     50 Number of Observations:                            33
     51 
     52 
     53 
     54 
     55 
     56 
     57 
     58 
     59 
     60 Data:  y               x
     61       8.440000E-01    0.000000E+00
     62       9.080000E-01    1.000000E+01
     63       9.320000E-01    2.000000E+01
     64       9.360000E-01    3.000000E+01
     65       9.250000E-01    4.000000E+01
     66       9.080000E-01    5.000000E+01
     67       8.810000E-01    6.000000E+01
     68       8.500000E-01    7.000000E+01
     69       8.180000E-01    8.000000E+01
     70       7.840000E-01    9.000000E+01
     71       7.510000E-01    1.000000E+02
     72       7.180000E-01    1.100000E+02
     73       6.850000E-01    1.200000E+02
     74       6.580000E-01    1.300000E+02
     75       6.280000E-01    1.400000E+02
     76       6.030000E-01    1.500000E+02
     77       5.800000E-01    1.600000E+02
     78       5.580000E-01    1.700000E+02
     79       5.380000E-01    1.800000E+02
     80       5.220000E-01    1.900000E+02
     81       5.060000E-01    2.000000E+02
     82       4.900000E-01    2.100000E+02
     83       4.780000E-01    2.200000E+02
     84       4.670000E-01    2.300000E+02
     85       4.570000E-01    2.400000E+02
     86       4.480000E-01    2.500000E+02
     87       4.380000E-01    2.600000E+02
     88       4.310000E-01    2.700000E+02
     89       4.240000E-01    2.800000E+02
     90       4.200000E-01    2.900000E+02
     91       4.140000E-01    3.000000E+02
     92       4.110000E-01    3.100000E+02
     93       4.060000E-01    3.200000E+02
     94