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Running /home/krg/bin/profile.do ...
Compile number 170082
. do benn5.do
. /* NIST/ITL StRD
> Dataset Name: Bennett5 (Bennett5.dat)
>
> File Format: ASCII
> Starting Values (lines 41 to 43)
> Certified Values (lines 41 to 48)
> Data (lines 61 to 214)
>
> Procedure: Nonlinear Least Squares Regression
>
> Description: These data are the result of a NIST study involving
> superconductivity magnetization modeling. The
> response variable is magnetism, and the predictor
> variable is the log of time in minutes.
>
> Reference: Bennett, L., L. Swartzendruber, and H. Brown,
> NIST (1994).
> Superconductivity Magnetization Modeling.
>
>
>
>
>
>
> Data: 1 Response Variable (y = magnetism)
> 1 Predictor Variable (x = log[time])
> 154 Observations
> Higher Level of Difficulty
> Observed Data
>
> Model: Miscellaneous Class
> 3 Parameters (b1 to b3)
>
> y = b1 * (b2+x)**(-1/b3) + e
>
>
>
> Starting values Certified Values
>
> Start 1 Start 2 Parameter Standard Deviation
> b1 = -2000 -1500 -2.5235058043E+03 2.9715175411E+02
> b2 = 50 45 4.6736564644E+01 1.2448871856E+00
> b3 = 0.8 0.85 9.3218483193E-01 2.0272299378E-02
>
> Residual Sum of Squares: 5.2404744073E-04
> Residual Standard Deviation: 1.8629312528E-03
> Degrees of Freedom: 151
> Number of Observations: 154
> */
.
. clear
.
. scalar N = 154
. scalar df_r = 151
. scalar df_m = 3
.
. scalar rss = 5.2404744073E-04
. scalar rmse = 1.8629312528E-03
.
. scalar b1 = -2.5235058043E+03
. scalar seb1 = 2.9715175411E+02
. scalar b2 = 4.6736564644E+01
. scalar seb2 = 1.2448871856E+00
. scalar b3 = 9.3218483193E-01
. scalar seb3 = 2.0272299378E-02
.
. qui input double(y x)
.
. nl ( y = {b1} * ({b2}+x)^(-1/{b3}) ), init(b1 -2000 b2 50 b3 0.8) eps(1e-10)
(obs = 154)
Iteration 0: residual SS = 15065.54
Iteration 1: residual SS = 13455.22
Iteration 2: residual SS = 11044.79
Iteration 3: residual SS = 1090.961
Iteration 4: residual SS = 1.168292
Iteration 5: residual SS = .0005302
Iteration 6: residual SS = .000524
Iteration 7: residual SS = .000524
Iteration 8: residual SS = .000524
Iteration 9: residual SS = .000524
Iteration 10: residual SS = .000524
Source | SS df MS
-------------+---------------------------------- Number of obs = 154
Model | 161368.38 3 53789.4586 R-squared = 1.0000
Residual | .00052405 151 3.4705e-06 Adj R-squared = 1.0000
-------------+---------------------------------- Root MSE = .0018629
Total | 161368.38 154 1047.8466 Res. dev. = -1501.963
------------------------------------------------------------------------------
y | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
/b1 | -2523.505 297.1516 -8.49 0.000 -3110.617 -1936.393
/b2 | 46.73656 1.244889 37.54 0.000 44.27691 49.19621
/b3 | .9321849 .0202723 45.98 0.000 .8921309 .9722389
------------------------------------------------------------------------------
.
. 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 () /*
> */ [b1]_se[_cons] seb1 [b2]_se[_cons] seb2 [b3]_se[_cons] seb3 () /*
> */ e(rmse) rmse e(rss) rss
[b1]_b[_cons] 6.4
[b2]_b[_cons] 7.0
[b3]_b[_cons] 7.1
-------------------------
min 6.4
[b1]_se[_cons] 6.3
[b2]_se[_cons] 5.9
[b3]_se[_cons] 6.2
-------------------------
min 5.9
e(rmse) 10.6
e(rss) 11.0
.
.
end of do-file