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         Licensed to:  Brian P. Poi, Ph.D.
                       StataCorp LP

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 misra1d.do 

. /* NIST/ITL StRD
> Dataset Name:  Misra1d           (Misra1d.dat)
> 
> File Format:   ASCII
>                Starting Values   (lines 41 to 42)
>                Certified Values  (lines 41 to 47)
>                Data              (lines 61 to 74)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   These data are the result of a NIST study regarding
>                dental research in monomolecular adsorption.  The
>                response variable is volume, and the predictor
>                variable is pressure.
> 
> Reference:     Misra, D., NIST (1978).  
>                Dental Research Monomolecular Adsorption Study.
> 
> 
> 
> 
> 
> 
> 
> Data:          1 Response  (y = volume)
>                1 Predictor (x = pressure)
>                14 Observations
>                Average Level of Difficulty
>                Observed Data
> 
> Model:         Miscellaneous Class
>                2 Parameters (b1 and b2)
> 
>                y = b1*b2*x*((1+b2*x)**(-1))  +  e
> 
> 
> 
>           Starting values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =   500         450           4.3736970754E+02  3.6489174345E+00
>   b2 =     0.0001      0.0003      3.0227324449E-04  2.9334354479E-06
> 
> Residual Sum of Squares:                    5.6419295283E-02
> Residual Standard Deviation:                6.8568272111E-02
> Degrees of Freedom:                                12
> Number of Observations:                            14
> */
. 
. clear

. 
. scalar N         = 14

. scalar df_r      = 12

. scalar df_m      = 2

. 
. scalar rss       = 5.6419295283E-02

. scalar rmse      = 6.8568272111E-02

. 
. scalar b1        = 4.3736970754E+02  

. scalar seb1      = 3.6489174345E+00

. scalar b2        = 3.0227324449E-04  

. scalar seb2      = 2.9334354479E-06

. 
. qui input double(y x)

. 
. nl ( y = {b1}*{b2}*x*( (1 + {b2}*x)^(-1) ) ), init(b1 500 b2 0.0001) eps(1e-1
> 0)
(obs = 14)

Iteration 0:  residual SS =  11202.66
Iteration 1:  residual SS =  10602.92
Iteration 2:  residual SS =  9606.646
Iteration 3:  residual SS =   460.899
Iteration 4:  residual SS =  14.49105
Iteration 5:  residual SS =  .0801687
Iteration 6:  residual SS =  .0564193
Iteration 7:  residual SS =  .0564193
Iteration 8:  residual SS =  .0564193

      Source |       SS       df       MS            Number of obs =        14
-------------+------------------------------         F(  2,    12) =  3.52e+06
       Model |  33059.5767     2  16529.7883         Prob > F      =    0.0000
    Residual |  .056419295    12  .004701608         R-squared     =    1.0000
-------------+------------------------------         Adj R-squared =    1.0000
       Total |  33059.6331    14  2361.40236         Root MSE      =  .0685683
                                                     Res. dev.     = -37.46574

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          b1 |   437.3697   3.648916   119.86   0.000     429.4194      445.32
          b2 |   .0003023   2.93e-06   103.04   0.000     .0002959    .0003087
------------------------------------------------------------------------------
 (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]               9.4
_b[b2]               9.3
-------------------------
min                  9.3

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

e(rmse)             11.1
e(rss)              11.2

. 
. 
. 
end of do-file