/* 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. 549-565. 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 1.00000 0 1.11111 1 1.24992 2 1.42753 3 1.65984 4 1.96875 5 2.38336 6 2.94117 7 3.68928 8 4.68559 9 6.00000 10 7.71561 11 9.92992 12 12.75603 13 16.32384 14 20.78125 15 26.29536 16 33.05367 17 41.26528 18 51.16209 19 63.00000 20 end gen int x2 = x1*x1 gen long x3 = x1*x2 gen long x4 = x1*x3 gen long x5 = x1*x4 reg y x1-x5 di "R-squared = " %20.15f e(r2) 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