___ ____ ____ ____ ____ (R) /__ / ____/ / ____/ ___/ / /___/ / /___/ 16.0 Copyright 1985-2019 StataCorp LLC Statistics/Data Analysis StataCorp 4905 Lakeway Drive Special Edition College Station, Texas 77845 USA 800-STATA-PC http://www.stata.com 979-696-4600 stata@stata.com 979-696-4601 (fax) 10-user Stata network perpetual license: Serial number: 1 Licensed to: Stata Developer StataCorp LLC Notes: 1. Stata is running in batch mode. 2. Unicode is supported; see help unicode_advice. 3. Maximum number of variables is set to 5000; see help set_maxvar. running /home/krg/bin/profile.do ... Compile number 785 . do longley.do . /* NIST/ITL StRD > > Linear Regression > > Difficulty=Higher Multilinear k=7 N=16 Observed > > Dataset Name: Longley (longley.dat) > > Procedure: Linear Least Squares Regression > > Reference: Longley, J. W. (1967). > An Appraisal of Least Squares Programs for the > Electronic Computer from the Viewpoint of the User. > Journal of the American Statistical Association, 62, pp. 819-8 > 41. > > Data: 1 Response Variable (y) > 6 Predictor Variable (x) > 16 Observations > Higher Level of Difficulty > Observed Data > > Model: Polynomial Class > 7 Parameters (B0,B1,...,B7) > > y = B0 + B1*x1 + B2*x2 + B3*x3 + B4*x4 + B5*x5 + B6*x6 + e > > > Certified Regression Statistics > > Standard Deviation > Parameter Estimate of Estimate > > B0 -3482258.63459582 890420.383607373 > B1 15.0618722713733 84.9149257747669 > B2 -0.358191792925910E-01 0.334910077722432E-01 > B3 -2.02022980381683 0.488399681651699 > B4 -1.03322686717359 0.214274163161675 > B5 -0.511041056535807E-01 0.226073200069370 > B6 1829.15146461355 455.478499142212 > > Residual > Standard Deviation 304.854073561965 > > R-Squared 0.995479004577296 > > > Certified Analysis of Variance Table > > Source of Degrees of Sums of Mean > Variation Freedom Squares Squares F Statistic > > Regression 6 184172401.944494 30695400.3240823 330.285339234588 > Residual 9 836424.055505915 92936.0061673238 > */ . . clear . . scalar N = 16 . scalar df_r = 9 . scalar df_m = 6 . . scalar rmse = 304.854073561965 . scalar r2 = 0.995479004577296 . scalar mss = 184172401.944494 . scalar F = 330.285339234588 . scalar rss = 836424.055505915 . . scalar b_cons = -3482258.63459582 . scalar se_cons = 890420.383607373 . scalar bx1 = 15.0618722713733 . scalar sex1 = 84.9149257747669 . scalar bx2 = -0.358191792925910E-01 . scalar sex2 = 0.334910077722432E-01 . scalar bx3 = -2.02022980381683 . scalar sex3 = 0.488399681651699 . scalar bx4 = -1.03322686717359 . scalar sex4 = 0.214274163161675 . scalar bx5 = -0.511041056535807E-01 . scalar sex5 = 0.226073200069370 . scalar bx6 = 1829.15146461355 . scalar sex6 = 455.478499142212 . . qui input double (y x1 x2 x3 x4 x5 x6) . . reg y x1-x6 Source | SS df MS Number of obs = 16 -------------+---------------------------------- F(6, 9) = 330.29 Model | 184172402 6 30695400.3 Prob > F = 0.0000 Residual | 836424.056 9 92936.0062 R-squared = 0.9955 -------------+---------------------------------- Adj R-squared = 0.9925 Total | 185008826 15 12333921.7 Root MSE = 304.85 ------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x1 | 15.06187 84.91493 0.18 0.863 -177.029 207.1528 x2 | -.0358192 .033491 -1.07 0.313 -.1115811 .0399427 x3 | -2.02023 .4883997 -4.14 0.003 -3.125067 -.915393 x4 | -1.033227 .2142742 -4.82 0.001 -1.517949 -.548505 x5 | -.0511041 .2260732 -0.23 0.826 -.5625172 .460309 x6 | 1829.151 455.4785 4.02 0.003 798.7875 2859.515 _cons | -3482259 890420.4 -3.91 0.004 -5496529 -1467988 ------------------------------------------------------------------------------ . di "R-squared = " %20.15f e(r2) R-squared = 0.995479004577295 . . 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[x4] bx4 _b[x5] bx5 _b[x6] bx6 () /* > */ _se[_cons] se_cons _se[x1] sex1 _se[x2] sex2 /* > */ _se[x3] sex3 _se[x4] sex4 _se[x4] sex4 _se[x5] sex5 _se[x6] sex6 () /* > */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss _b[_cons] 13.2 _b[x1] 12.3 _b[x2] 12.6 _b[x3] 13.3 _b[x4] 13.6 _b[x4] 13.6 _b[x5] 12.1 _b[x6] 13.2 ------------------------- min 12.1 _se[_cons] 13.0 _se[x1] 12.9 _se[x2] 12.9 _se[x3] 12.9 _se[x4] 13.2 _se[x4] 13.2 _se[x5] 12.9 _se[x6] 13.0 ------------------------- min 12.9 e(rmse) 13.5 e(r2) 15.2 e(mss) 15.8 e(F) 13.2 e(rss) 13.2 . . . end of do-file