___ ____ ____ ____ ____ 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 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 | 161368.376 3 53789.4586 R-squared = 1.0000 Residual | .000524047 151 3.4705e-06 Adj R-squared = 1.0000 -------------+------------------------------ Root MSE = .0018629 Total | 161368.376 154 1047.8466 Res. dev. = -1501.963 ------------------------------------------------------------------------------ y | Coef. 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