___ ____ ____ ____ ____ 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 wampler2.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-2 (wampler2.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 0.000000000000000 > B1 0.100000000000000 0.000000000000000 > B2 0.100000000000000E-01 0.000000000000000 > B3 0.100000000000000E-02 0.000000000000000 > B4 0.100000000000000E-03 0.000000000000000 > B5 0.100000000000000E-04 0.000000000000000 > > Residual > Standard Deviation 0.000000000000000 > R-Squared 1.00000000000000 > > > Certified Analysis of Variance Table > > Source of Degrees of Sums of Mean > Variation Freedom Squares Squares F Statistic > > Regression 5 6602.91858365167 1320.58371673033 Infinity > Residual 15 0.000000000000000 0.000000000000000 > */ . . clear . . scalar N = 21 . scalar df_r = 15 . scalar df_m = 5 . . scalar rmse = 0 . scalar r2 = 1 . scalar mss = 6602.91858365167 . scalar F = . . scalar rss = 0 . . scalar b_cons = 1 . scalar se_cons = 0 . scalar bx1 = 1e-1 . scalar sex1 = 0 . scalar bx2 = 1e-2 . scalar sex2 = 0 . scalar bx3 = 1e-3 . scalar sex3 = 0 . scalar bx4 = 1e-4 . scalar sex4 = 0 . scalar bx5 = 1e-5 . scalar sex5 = 0 . . qui input double 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) = . Model | 6602.91858 5 1320.58372 Prob > F = . Residual | 0 15 0 R-squared = 1.0000 -------------+------------------------------ Adj R-squared = 1.0000 Total | 6602.91858 20 330.145929 Root MSE = 0 ------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | .1 . . . . . x2 | .01 . . . . . x3 | .001 . . . . . x4 | .0001 . . . . . x5 | 1.00e-05 . . . . . _cons | 1 . . . . . ------------------------------------------------------------------------------ . di "R-squared = " %20.15f e(r2) R-squared = 1.000000000000000 . . 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] 11.7 _b[x1] 10.4 _b[x2] 9.9 _b[x3] 9.7 _b[x4] 10.0 _b[x5] 10.7 ------------------------- min 9.7 _se[_cons] _se[x1] _se[x2] _se[x3] _se[x4] _se[x5] ------------------------- min 15.0 e(rmse) e(r2) e(mss) 15.4 e(F) e(rss) . . end of do-file