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Running /home/krg/bin/profile.do ...
Compile number 170082

. do kirby2.do 

. /* NIST/ITL StRD
> 
> Nonlinear Regression
> 
> Difficulty=Average  Rational  k=5  N=151  Observed
> 
> Dataset Name:  Kirby2            (Kirby2.dat)
> 
> Procedure:     Nonlinear Least Squares Regression
> 
> Description:   These data are the result of a NIST study involving
>                scanning electron microscope line with standards.
> 
> 
> Reference:     Kirby, R., NIST (197?).
>                Scanning electron microscope line width standards.
> 
> Data:          1 Response  (y)
>                1 Predictor (x)
>                151 Observations
>                Average Level of Difficulty
>                Observed Data
> 
> Model:         Rational Class (quadratic/quadratic)
>                5 Parameters (b1 to b5)
> 
>                y = (b1 + b2*x + b3*x**2) /
>                    (1 + b4*x + b5*x**2)  +  e
> 
> 
>           Starting values                  Certified Values
> 
>         Start 1     Start 2           Parameter     Standard Deviation
>   b1 =    2           1.5          1.6745063063E+00  8.7989634338E-02
>   b2 =   -0.1        -0.15        -1.3927397867E-01  4.1182041386E-03
>   b3 =    0.003       0.0025       2.5961181191E-03  4.1856520458E-05
>   b4 =   -0.001      -0.0015      -1.7241811870E-03  5.8931897355E-05
>   b5 =    0.00001     0.00002      2.1664802578E-05  2.0129761919E-07
> 
> Residual Sum of Squares:                    3.9050739624E+00
> Residual Standard Deviation:                1.6354535131E-01
> Degrees of Freedom:                               146
> Number of Observations:                           151
> */
. 
. clear

. 
. scalar N         = 151

. scalar df_r      = 146

. scalar df_m      = 5

. 
. scalar rss       = 3.9050739624E+00

. scalar rmse      = 1.6354535131E-01

. 
. scalar b1        = 1.6745063063E+00

. scalar seb1      = 8.7989634338E-02

. scalar b2        = -1.3927397867E-01

. scalar seb2      = 4.1182041386E-03

. scalar b3        = 2.5961181191E-03

. scalar seb3      = 4.1856520458E-05

. scalar b4        = -1.7241811870E-03

. scalar seb4      = 5.8931897355E-05

. scalar b5        = 2.1664802578E-05

. scalar seb5      = 2.0129761919E-07

. 
. qui input double (y x)

. 
. nl ( y = ({b1} + {b2}*x + {b3}*x^2) / (1 + {b4}*x + {b5}*x^2) ), ///
>         init(b1 2 b2 -0.1 b3 0.003 b4 -0.001 b5 0.00001) eps(1e-10)
(obs = 151)

Iteration 0:  residual SS =  31570.49
Iteration 1:  residual SS =  640.0434
Iteration 2:  residual SS =  7.214178
Iteration 3:  residual SS =   3.90511
Iteration 4:  residual SS =  3.905074
Iteration 5:  residual SS =  3.905074
Iteration 6:  residual SS =  3.905074
Iteration 7:  residual SS =  3.905074
Iteration 8:  residual SS =  3.905074
Iteration 9:  residual SS =  3.905074
Iteration 10:  residual SS =  3.905074


      Source |      SS            df       MS
-------------+----------------------------------    Number of obs =        151
       Model |  490245.12          5  98049.0238    R-squared     =     1.0000
    Residual |   3.905074        146  .026747082    Adj R-squared =     1.0000
-------------+----------------------------------    Root MSE      =   .1635454
       Total |  490249.02        151  3246.68228    Res. dev.     =   -123.386

------------------------------------------------------------------------------
           y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         /b1 |   1.674506   .0879896    19.03   0.000     1.500608    1.848404
         /b2 |   -.139274   .0041182   -33.82   0.000     -.147413    -.131135
         /b3 |   .0025961   .0000419    62.02   0.000     .0025134    .0026788
         /b4 |  -.0017242   .0000589   -29.26   0.000    -.0018407   -.0016077
         /b5 |   .0000217   2.01e-07   107.63   0.000     .0000213    .0000221
------------------------------------------------------------------------------

. 
. assert N    == e(N)

. assert df_r == e(df_r)

. assert df_m == e(df_m)

. 
. lrecomp [b1]_b[_cons] b1 [b2]_b[_cons] b2 [b3]_b[_cons] b3 /*
> */ [b4]_b[_cons] b4 [b5]_b[_cons] b5 () /*
> */ [b1]_se[_cons] seb1 [b2]_se[_cons] seb2 [b3]_se[_cons] seb3 /*
> */ [b4]_se[_cons] seb4 [b5]_se[_cons] seb5 () /*
> */ e(rmse) rmse e(rss) rss

[b1]_b[_cons]        8.0
[b2]_b[_cons]        8.2
[b3]_b[_cons]        8.4
[b4]_b[_cons]        8.2
[b5]_b[_cons]        8.6
-------------------------
min                  8.0

[b1]_se[_cons]       8.1
[b2]_se[_cons]       7.9
[b3]_se[_cons]       7.9
[b4]_se[_cons]       7.8
[b5]_se[_cons]       6.3
-------------------------
min                  6.3

e(rmse)             10.8
e(rss)              11.6

. 
end of do-file