___ ____ ____ ____ ____ 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 wampler5.do . /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ > > Linear Regression > > Difficulty=Higher Polynomial k=6 N=21 Generated > > Dataset Name: Wampler-5 (wampler5.dat) > > Procedure: Linear Least Squares Regression > > Reference: Wampler, R. H. (1970). > A Report of the Accuracy of Some Widely-Used Least > Squares Computer Programs. > Journal of the American Statistical Association, 65, pp. 549-5 > 65. > > Data: 1 Response Variable (y) > 1 Predictor Variable (x) > 21 Observations > Higher Level of Difficulty > Generated Data > > Model: Polynomial Class > 6 Parameters (B0,B1,...,B5) > > y = B0 + B1*x + B2*(x**2) + B3*(x**3)+ B4*(x**4) + B5*(x**5) > > > > Certified Regression Statistics > > Standard Deviation > Parameter Estimate of Estimate > > B0 1.00000000000000 21523262.4678170 > B1 1.00000000000000 23635517.3469681 > B2 1.00000000000000 7793435.24331583 > B3 1.00000000000000 1014755.07550350 > B4 1.00000000000000 56456.6512170752 > B5 1.00000000000000 1123.24854679312 > > Residual > Standard Deviation 23601450.2379268 > > R-Squared 0.224668921574940E-02 > > > Certified Analysis of Variance Table > > Source of Degrees of Sums of Mean > Variation Freedom Squares Squares F Statistic > > Regression 5 18814317208116.7 3762863441623.33 6.7552445824012241E > -03 > Residual 15 0.835542680000000E+16 557028453333333. > */ . . clear . . scalar N = 21 . scalar df_r = 15 . scalar df_m = 5 . . scalar rmse = 23601450.2379268 . scalar r2 = 0.224668921574940E-02 . scalar mss = 18814317208116.7 . scalar F = 6.7552445824012241E-03 . scalar rss = 0.835542680000000E+16 . . scalar b_cons = 1 . scalar se_cons = 21523262.4678170 . scalar bx1 = 1 . scalar sex1 = 23635517.3469681 . scalar bx2 = 1 . scalar sex2 = 7793435.24331583 . scalar bx3 = 1 . scalar sex3 = 1014755.07550350 . scalar bx4 = 1 . scalar sex4 = 56456.6512170752 . scalar bx5 = 1 . scalar sex5 = 1123.24854679312 . . qui input long y byte x1 . . gen int x2 = x1*x1 . gen long x3 = x1*x2 . gen long x4 = x1*x3 . gen long x5 = x1*x4 . . reg y x1-x5 Source | SS df MS Number of obs = 21 -------------+------------------------------ F( 5, 15) = 0.01 Model | 1.8814e+13 5 3.7629e+12 Prob > F = 1.0000 Residual | 8.3554e+15 15 5.5703e+14 R-squared = 0.0022 -------------+------------------------------ Adj R-squared = -0.3303 Total | 8.3742e+15 20 4.1871e+14 Root MSE = 2.4e+07 ------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .9999996 2.36e+07 0.00 1.000 -5.04e+07 5.04e+07 x2 | 1 7793435 0.00 1.000 -1.66e+07 1.66e+07 x3 | 1 1014755 0.00 1.000 -2162898 2162900 x4 | 1 56456.65 0.00 1.000 -120333.5 120335.5 x5 | 1 1123.249 0.00 0.999 -2393.148 2395.148 _cons | 1 2.15e+07 0.00 1.000 -4.59e+07 4.59e+07 ------------------------------------------------------------------------------ . di "R-squared = " %20.15f e(r2) R-squared = 0.002246689215749 . . assert N == e(N) . assert df_r == e(df_r) . assert df_m == e(df_m) . . lrecomp _b[_cons] b_cons _b[x1] bx1 _b[x2] bx2 /* > */ _b[x3] bx3 _b[x4] bx4 _b[x5] bx5 () /* > */ _se[_cons] se_cons _se[x1] sex1 _se[x2] sex2 /* > */ _se[x3] sex3 _se[x4] sex4 _se[x5] sex5 () /* > */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss _b[_cons] 6.6 _b[x1] 6.4 _b[x2] 6.8 _b[x3] 7.7 _b[x4] 9.0 _b[x5] 10.7 ------------------------- min 6.4 _se[_cons] 11.4 _se[x1] 10.9 _se[x2] 10.8 _se[x3] 10.8 _se[x4] 10.8 _se[x5] 10.8 ------------------------- min 10.8 e(rmse) 14.8 e(r2) 13.7 e(mss) 13.8 e(F) 13.7 e(rss) . . end of do-file