/* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ Linear Regression Difficulty=Higher Polynomial k=6 N=21 Generated Dataset Name: Wampler-3 (wampler3.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 2152.32624678170 B1 1.00000000000000 2363.55173469681 B2 1.00000000000000 779.343524331583 B3 1.00000000000000 101.475507550350 B4 1.00000000000000 5.64566512170752 B5 1.00000000000000 0.112324854679312 Residual Standard Deviation 2360.14502379268 R-Squared 0.999995559025820 Certified Analysis of Variance Table Source of Degrees of Sums of Mean Variation Freedom Squares Squares F Statistic Regression 5 18814317208116.7 3762863441623.33 675524.458240122 Residual 15 83554268.0000000 5570284.53333333 */ clear scalar N = 21 scalar df_r = 15 scalar df_m = 5 scalar rmse = 2360.14502379268 scalar r2 = 0.999995559025820 scalar mss = 18814317208116.7 scalar F = 675524.458240122 scalar rss = 83554268.0000000 scalar b_cons = 1 scalar se_cons = 2152.32624678170 scalar bx1 = 1 scalar sex1 = 2363.55173469681 scalar bx2 = 1 scalar sex2 = 779.343524331583 scalar bx3 = 1 scalar sex3 = 101.475507550350 scalar bx4 = 1 scalar sex4 = 5.64566512170752 scalar bx5 = 1 scalar sex5 = 0.112324854679312 qui input long y byte x1 760 0 -2042 1 2111 2 -1684 3 3888 4 1858 5 11379 6 17560 7 39287 8 64382 9 113159 10 175108 11 273291 12 400186 13 581243 14 811568 15 1121004 16 1506550 17 2002767 18 2611612 19 3369180 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