___ ____ ____ ____ ____ 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