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Notes:
      1.  (-m# option or -set memory-) 1.00 MB allocated to data
      2.  Command line editing disabled
      3.  Stata running in batch mode


running /home/bpp/bin/profile.do ...

. do dwood.do 

. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
> 
> Nonlinear Regression
> 
> Difficulty=Lower  Miscellaneous  k=2  N=6  Observed
> 
> Dataset Name:  DanielWood        (DanielWood.dat)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   These data and model are described in Daniel and Wood
>                (1980), and originally published in E.S.Keeping,
>                "Introduction to Statistical Inference," Van Nostrand
>                Company, Princeton, NJ, 1962, p. 354.  The response
>                variable is energy radieted from a carbon filament
>                lamp per cm**2 per second, and the predictor variable
>                is the absolute temperature of the filament in 1000
>                degrees Kelvin.
> 
> Reference:     Daniel, C. and F. S. Wood (1980).
>                Fitting Equations to Data, Second Edition.
>                New York, NY:  John Wiley and Sons, pp. 428-431.
> 
> 
> Data:          1 Response Variable  (y = energy)
>                1 Predictor Variable (x = temperature)
>                6 Observations
>                Lower Level of Difficulty
>                Observed Data
> 
> Model:         Miscellaneous Class
>                2 Parameters (b1 and b2)
> 
>                y  = b1*x**b2  +  e
> 
> 
> 
>           Starting values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =   1           0.7           7.6886226176E-01  1.8281973860E-02
>   b2 =   5           4             3.8604055871E+00  5.1726610913E-02
> 
> Residual Sum of Squares:                    4.3173084083E-03
> Residual Standard Deviation:                3.2853114039E-02
> Degrees of Freedom:                                4
> Number of Observations:                            6
> */
. 
. clear

. 
. scalar N         = 6

. scalar df_r      = 4

. scalar df_m      = 2

. 
. scalar rss       = 4.3173084083E-03

. scalar rmse      = 3.2853114039E-02

. 
. scalar b1        = 7.6886226176E-01

. scalar seb1      = 1.8281973860E-02

. scalar b2        = 3.8604055871E+00

. scalar seb2      = 5.1726610913E-02

. 
. qui input double (y x)

. 
. nl ( y  = {b1}*x^{b2} ), init(b1 1 b2 5) eps(1e-10) 
(obs = 6)

Iteration 0:  residual SS =  149.7192
Iteration 1:  residual SS =  4.610882
Iteration 2:  residual SS =  .0316845
Iteration 3:  residual SS =  .0043206
Iteration 4:  residual SS =  .0043173
Iteration 5:  residual SS =  .0043173
Iteration 6:  residual SS =  .0043173
Iteration 7:  residual SS =  .0043173
Iteration 8:  residual SS =  .0043173

      Source |       SS       df       MS            Number of obs =         6
-------------+------------------------------         F(  2,     4) =  48138.09
       Model |  103.913501     2  51.9567503         Prob > F      =    0.0000
    Residual |  .004317308     4  .001079327         R-squared     =    1.0000
-------------+------------------------------         Adj R-squared =    0.9999
       Total |  103.917818     6  17.3196363         Root MSE      =  .0328531
                                                     Res. dev.     = -26.39403

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          b1 |   .7688623    .018282    42.06   0.000     .7181034    .8196211
          b2 |   3.860406   .0517266    74.63   0.000      3.71679    4.004022
------------------------------------------------------------------------------
 (SEs, P values, CIs, and correlations are asymptotic approximations)

. 
. assert N    == e(N)

. assert df_r == e(df_r)

. assert df_m == e(df_m)

. 
. lrecomp _b[b1] b1 _b[b2] b2 () /*
> */ _se[b1] seb1 _se[b2] seb2 () /*
> */ e(rmse) rmse e(rss) rss

_b[b1]               8.6
_b[b2]               8.8
-------------------------
min                  8.6

_se[b1]              6.5
_se[b2]              6.2
-------------------------
min                  6.2

e(rmse)             11.2
e(rss)              11.7

. 
end of do-file