/* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ Linear Regression Difficulty=Higher Polynomial k=6 N=21 Generated Dataset Name: Wampler-4 (wampler4.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 215232.624678170 B1 1.00000000000000 236355.173469681 B2 1.00000000000000 77934.3524331583 B3 1.00000000000000 10147.5507550350 B4 1.00000000000000 564.566512170752 B5 1.00000000000000 11.2324854679312 Residual Standard Deviation 236014.502379268 R-Squared 0.957478440825662 Certified Analysis of Variance Table Source of Degrees of Sums of Mean Variation Freedom Squares Squares F Statistic Regression 5 18814317208116.7 3762863441623.33 67.5524458240122 Residual 15 835542680000.000 55702845333.3333 */ clear scalar N = 21 scalar df_r = 15 scalar df_m = 5 scalar rmse = 236014.502379268 scalar r2 = 0.957478440825662 scalar mss = 18814317208116.7 scalar F = 67.5524458240122 scalar rss = 835542680000.000 scalar b_cons = 1 scalar se_cons = 215232.624678170 scalar bx1 = 1 scalar sex1 = 236355.173469681 scalar bx2 = 1 scalar sex2 = 77934.3524331583 scalar bx3 = 1 scalar sex3 = 10147.5507550350 scalar bx4 = 1 scalar sex4 = 564.566512170752 scalar bx5 = 1 scalar sex5 = 11.2324854679312 qui input long y byte x1 75901 0 -204794 1 204863 2 -204436 3 253665 4 -200894 5 214131 6 -185192 7 221249 8 -138370 9 315911 10 -27644 11 455253 12 197434 13 783995 14 608816 15 1370781 16 1303798 17 2205519 18 2408860 19 3444321 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