___ ____ ____ ____ ____ tm
/__ / ____/ / ____/
___/ / /___/ / /___/ 10.0 Copyright 1984-2007
Statistics/Data Analysis StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-STATA-PC http://www.stata.com
979-696-4600 stata@stata.com
979-696-4601 (fax)
3-user Stata for Linux64 (network) perpetual license:
Serial number: 999
Licensed to: Brian P. Poi, PhD
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 thurber.do
. /* NIST/ITL StRD
> Dataset Name: Thurber (Thurber.dat)
>
> File Format: ASCII
> Starting Values (lines 41 to 47)
> Certified Values (lines 41 to 52)
> Data (lines 61 to 97)
>
> Procedure: Nonlinear Least Squares Regression
>
> Description: These data are the result of a NIST study involving
> semiconductor electron mobility. The response
> variable is a measure of electron mobility, and the
> predictor variable is the natural log of the density.
>
>
> Reference: Thurber, R., NIST (197?).
> Semiconductor electron mobility modeling.
>
>
>
>
>
>
> Data: 1 Response Variable (y = electron mobility)
> 1 Predictor Variable (x = log[density])
> 37 Observations
> Higher Level of Difficulty
> Observed Data
>
> Model: Rational Class (cubic/cubic)
> 7 Parameters (b1 to b7)
>
> y = (b1 + b2*x + b3*x**2 + b4*x**3) /
> (1 + b5*x + b6*x**2 + b7*x**3) + e
>
>
> Starting Values Certified Values
>
> Start 1 Start 2 Parameter Standard Deviation
> b1 = 1000 1300 1.2881396800E+03 4.6647963344E+00
> b2 = 1000 1500 1.4910792535E+03 3.9571156086E+01
> b3 = 400 500 5.8323836877E+02 2.8698696102E+01
> b4 = 40 75 7.5416644291E+01 5.5675370270E+00
> b5 = 0.7 1 9.6629502864E-01 3.1333340687E-02
> b6 = 0.3 0.4 3.9797285797E-01 1.4984928198E-02
> b7 = 0.03 0.05 4.9727297349E-02 6.5842344623E-03
>
> Residual Sum of Squares: 5.6427082397E+03
> Residual Standard Deviation: 1.3714600784E+01
> Degrees of Freedom: 30
> Number of Observations: 37
> */
.
. clear
.
. scalar N = 37
. scalar df_r = 30
. scalar df_m = 7
.
. scalar rss = 5.6427082397E+03
. scalar rmse = 1.3714600784E+01
.
. scalar b1 = 1.2881396800E+03
. scalar seb1 = 4.6647963344E+00
. scalar b2 = 1.4910792535E+03
. scalar seb2 = 3.9571156086E+01
. scalar b3 = 5.8323836877E+02
. scalar seb3 = 2.8698696102E+01
. scalar b4 = 7.5416644291E+01
. scalar seb4 = 5.5675370270E+00
. scalar b5 = 9.6629502864E-01
. scalar seb5 = 3.1333340687E-02
. scalar b6 = 3.9797285797E-01
. scalar seb6 = 1.4984928198E-02
. scalar b7 = 4.9727297349E-02
. scalar seb7 = 6.5842344623E-03
.
. qui input double(y x)
.
. #delimit ;
delimiter now ;
. nl ( y = ({b1} + {b2}*x + {b3}*x^2 + {b4}*x^3) /
> (1 + {b5}*x + {b6}*x^2 + {b7}*x^3) ),
> init(b1 1000 b2 1000 b3 400 b4 40 b5 0.7 b6 0.3 b7 0.03)
> eps(1e-10) ;
(obs = 37)
Iteration 0: residual SS = 2917801
Iteration 1: residual SS = 2098402
Iteration 2: residual SS = 1704578
Iteration 3: residual SS = 820189.6
Iteration 4: residual SS = 161224.5
Iteration 5: residual SS = 42863.14
Iteration 6: residual SS = 17223.33
Iteration 7: residual SS = 6048.551
Iteration 8: residual SS = 5664.019
Iteration 9: residual SS = 5650.229
Iteration 10: residual SS = 5644.848
Iteration 11: residual SS = 5643.912
Iteration 12: residual SS = 5643.166
Iteration 13: residual SS = 5642.939
Iteration 14: residual SS = 5642.805
Iteration 15: residual SS = 5642.754
Iteration 16: residual SS = 5642.728
Iteration 17: residual SS = 5642.718
Iteration 18: residual SS = 5642.712
Iteration 19: residual SS = 5642.71
Iteration 20: residual SS = 5642.709
Iteration 21: residual SS = 5642.709
Iteration 22: residual SS = 5642.708
Iteration 23: residual SS = 5642.708
Iteration 24: residual SS = 5642.708
Iteration 25: residual SS = 5642.708
Iteration 26: residual SS = 5642.708
Iteration 27: residual SS = 5642.708
Iteration 28: residual SS = 5642.708
Iteration 29: residual SS = 5642.708
Iteration 30: residual SS = 5642.708
Iteration 31: residual SS = 5642.708
Iteration 32: residual SS = 5642.708
Iteration 33: residual SS = 5642.708
Iteration 34: residual SS = 5642.708
Iteration 35: residual SS = 5642.708
Source | SS df MS
-------------+------------------------------ Number of obs = 37
Model | 34156447.9 7 4879492.56 R-squared = 0.9998
Residual | 5642.70824 30 188.090275 Adj R-squared = 0.9998
-------------+------------------------------ Root MSE = 13.7146
Total | 34162090.7 37 923299.748 Res. dev. = 291.0079
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
/b1 | 1288.14 4.664796 276.14 0.000 1278.613 1297.666
/b2 | 1491.079 39.57124 37.68 0.000 1410.264 1571.894
/b3 | 583.2382 28.69875 20.32 0.000 524.6276 641.8489
/b4 | 75.41662 5.567549 13.55 0.000 64.04617 86.78707
/b5 | .9662949 .0313334 30.84 0.000 .9023035 1.030286
/b6 | .3979728 .014985 26.56 0.000 .3673694 .4285762
/b7 | .0497273 .0065842 7.55 0.000 .0362805 .0631741
------------------------------------------------------------------------------
. #delimit cr
delimiter now cr
.
. 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 [b6]_b[_cons] b6 [b7]_b[_cons] b7 () /*
> */ [b1]_se[_cons] seb1 [b2]_se[_cons] seb2 [b3]_se[_cons] seb3 [b4]_se[_cons]
> seb4 /*
> */ [b5]_se[_cons] seb5 [b6]_se[_cons] seb6 [b7]_se[_cons] seb7 () /*
> */ e(rmse) rmse e(rss) rss
[b1]_b[_cons] 9.3
[b2]_b[_cons] 6.9
[b3]_b[_cons] 6.7
[b4]_b[_cons] 6.5
[b5]_b[_cons] 6.8
[b6]_b[_cons] 6.7
[b7]_b[_cons] 6.5
-------------------------
min 6.5
[b1]_se[_cons] 7.1
[b2]_se[_cons] 5.7
[b3]_se[_cons] 5.7
[b4]_se[_cons] 5.7
[b5]_se[_cons] 5.7
[b6]_se[_cons] 5.4
[b7]_se[_cons] 6.3
-------------------------
min 5.4
e(rmse) 10.6
e(rss) 11.3
.
.
.
end of do-file