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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 boxbod.do 

. /* NIST/ITL StRD
> Dataset Name:  BoxBOD            (BoxBOD.dat)
> 
> File Format:   ASCII
>                Starting Values   (lines 41 to 42)
>                Certified Values  (lines 41 to 47)
>                Data              (lines 61 to 66)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   These data are described in detail in Box, Hunter and
>                Hunter (1978).  The response variable is biochemical
>                oxygen demand (BOD) in mg/l, and the predictor
>                variable is incubation time in days.
> 
> 
> Reference:     Box, G. P., W. G. Hunter, and J. S. Hunter (1978).
>                Statistics for Experimenters.  
>                New York, NY: Wiley, pp. 483-487.
> 
> 
> 
> 
> 
> Data:          1 Response  (y = biochemical oxygen demand)
>                1 Predictor (x = incubation time)
>                6 Observations
>                Higher Level of Difficulty
>                Observed Data
> 
> Model:         Exponential Class
>                2 Parameters (b1 and b2)
> 
>                y = b1*(1-exp[-b2*x])  +  e
> 
> 
>  
>           Starting values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =   1           100           2.1380940889E+02  1.2354515176E+01
>   b2 =   1             0.75        5.4723748542E-01  1.0455993237E-01
> 
> Residual Sum of Squares:                    1.1680088766E+03
> Residual Standard Deviation:                1.7088072423E+01
> Degrees of Freedom:                                4
> Number of Observations:                            6  
> */
. 
. clear

. 
. scalar N         = 6  

. scalar df_r      = 4

. scalar df_m      = 2

. 
. scalar rss       = 1.1680088766E+03

. scalar rmse      = 1.7088072423E+01

. 
. scalar b1        = 2.1380940889E+02  

. scalar seb1      = 1.2354515176E+01

. scalar b2        = 5.4723748542E-01  

. scalar seb2      = 1.0455993237E-01

. 
. qui input double(y x)

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

Iteration 0:  residual SS =  186382.4
Iteration 1:  residual SS =    184700
Iteration 2:  residual SS =  13388.66
Iteration 3:  residual SS =   7327.64
Iteration 4:  residual SS =  2375.331
Iteration 5:  residual SS =  1388.389
Iteration 6:  residual SS =   1168.01
Iteration 7:  residual SS =  1168.009
Iteration 8:  residual SS =  1168.009
Iteration 9:  residual SS =  1168.009
Iteration 10:  residual SS =  1168.009
Iteration 11:  residual SS =  1168.009
Iteration 12:  residual SS =  1168.009
Iteration 13:  residual SS =  1168.009
Iteration 14:  residual SS =  1168.009
Iteration 15:  residual SS =  1168.009

      Source |       SS       df       MS            Number of obs =         6
-------------+------------------------------         F(  2,     4) =    320.44
       Model |  187140.991     2  93570.4956         Prob > F      =    0.0000
    Residual |  1168.00888     4  292.002219         R-squared     =    0.9938
-------------+------------------------------         Adj R-squared =    0.9907
       Total |      188309     6  31384.8333         Root MSE      =  17.08807
                                                     Res. dev.     =  48.65504

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          b1 |   213.8094   12.35451    17.31   0.000     179.5078     248.111
          b2 |   .5472375     .10456     5.23   0.006     .2569325    .8375425
------------------------------------------------------------------------------
 (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]               7.9
_b[b2]               7.3
-------------------------
min                  7.3

_se[b1]              6.8
_se[b2]              6.7
-------------------------
min                  6.7

e(rmse)             11.1
e(rss)              10.4

. 
. 
end of do-file