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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 roszman1.do 

. /* NIST/ITL StRD
> Dataset Name:  Roszman1          (Roszman1.dat)
> 
> File Format:   ASCII
>                Starting Values   (lines 41 to 44)
>                Certified Values  (lines 41 to 49)
>                Data              (lines 61 to 85)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   These data are the result of a NIST study involving
>                quantum defects in iodine atoms.  The response
>                variable is the number of quantum defects, and the
>                predictor variable is the excited energy state.
>                The argument to the ARCTAN function is in radians.
> 
> Reference:     Roszman, L., NIST (19??).  
>                Quantum Defects for Sulfur I Atom.
> 
> 
> 
> 
> 
> 
> Data:          1 Response  (y = quantum defect)
>                1 Predictor (x = excited state energy)
>                25 Observations
>                Average Level of Difficulty
>                Observed Data
> 
> Model:         Miscellaneous Class
>                4 Parameters (b1 to b4)
> 
>                pi = 3.141592653589793238462643383279E0
>                y =  b1 - b2*x - arctan[b3/(x-b4)]/pi  +  e
> 
> 
>           Starting Values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =      0.1         0.2         2.0196866396E-01  1.9172666023E-02
>   b2 =     -0.00001    -0.000005   -6.1953516256E-06  3.2058931691E-06
>   b3 =   1000        1200           1.2044556708E+03  7.4050983057E+01
>   b4 =   -100        -150          -1.8134269537E+02  4.9573513849E+01
> 
> Residual Sum of Squares:                    4.9484847331E-04
> Residual Standard Deviation:                4.8542984060E-03
> Degrees of Freedom:                                 21
> Number of Observations:                             25
> */
. 
. clear

. 
. scalar N         = 25

. scalar df_r      = 21

. scalar df_m      = 4

. 
. scalar rss       = 4.9484847331E-04

. scalar rmse      = 4.8542984060E-03

. 
. scalar b1        = 2.0196866396E-01  

. scalar seb1      = 1.9172666023E-02

. scalar b2        = -6.1953516256E-06  

. scalar seb2      = 3.2058931691E-06

. scalar b3        = 1.2044556708E+03  

. scalar seb3      = 7.4050983057E+01

. scalar b4        = -1.8134269537E+02  

. scalar seb4      = 4.9573513849E+01

. *scalar pi = 3.141592653589793238462643383279
. 
. qui input double(y x)

. 
. nl ( y =  {b1} - {b2}*x - atan({b3}/(x-{b4}))/_pi ) , ///
>         init(b1 0.1 b2 -0.00001 b3 1000 b4 -100) eps(1e-10) 
(obs = 25)

Iteration 0:  residual SS =  .5108107
Iteration 1:  residual SS =  .0006527
Iteration 2:  residual SS =  .0004949
Iteration 3:  residual SS =  .0004948
Iteration 4:  residual SS =  .0004948
Iteration 5:  residual SS =  .0004948
Iteration 6:  residual SS =  .0004948
Iteration 7:  residual SS =  .0004948

      Source |       SS       df       MS            Number of obs =        25
-------------+------------------------------         F(  4,    21) =  50335.87
       Model |  4.74450091     4  1.18612523         Prob > F      =    0.0000
    Residual |  .000494848    21  .000023564         R-squared     =    0.9999
-------------+------------------------------         Adj R-squared =    0.9999
       Total |  4.74499576    25   .18979983         Root MSE      =  .0048543
                                                     Res. dev.     = -199.8064

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          b1 |   .2019687   .0191727    10.53   0.000     .1620969    .2418404
          b2 |  -6.20e-06   3.21e-06    -1.93   0.067    -.0000129    4.72e-07
          b3 |   1204.456   74.05101    16.27   0.000     1050.458    1358.453
          b4 |  -181.3427   49.57353    -3.66   0.001    -284.4365    -78.2489
------------------------------------------------------------------------------
 (SEs, P values, CIs, and correlations are asymptotic approximations)

.         
. assert N    == e(N)

. 
. lrecomp _b[b1] b1 _b[b2] b2 _b[b3] b3 /*
> */ _b[b4] b4 () /*
> */ _se[b1] seb1 _se[b2] seb2 _se[b3] seb3 /*
> */ _se[b4] seb4 () /*
> */ e(rmse) rmse e(rss) rss

_b[b1]               8.1
_b[b2]               7.4
_b[b3]               8.2
_b[b4]               7.7
-------------------------
min                  7.4

_se[b1]              6.6
_se[b2]              6.8
_se[b3]              6.4
_se[b4]              6.5
-------------------------
min                  6.4

e(rmse)             11.3
e(rss)              12.2

. 
. 
end of do-file