/* NIST StRD benchmark from http://www.nist.gov/itl/div898/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. 819-841. 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) 60323 83.0 234289 2356 1590 107608 1947 61122 88.5 259426 2325 1456 108632 1948 60171 88.2 258054 3682 1616 109773 1949 61187 89.5 284599 3351 1650 110929 1950 63221 96.2 328975 2099 3099 112075 1951 63639 98.1 346999 1932 3594 113270 1952 64989 99.0 365385 1870 3547 115094 1953 63761 100.0 363112 3578 3350 116219 1954 66019 101.2 397469 2904 3048 117388 1955 67857 104.6 419180 2822 2857 118734 1956 68169 108.4 442769 2936 2798 120445 1957 66513 110.8 444546 4681 2637 121950 1958 68655 112.6 482704 3813 2552 123366 1959 69564 114.2 502601 3931 2514 125368 1960 69331 115.7 518173 4806 2572 127852 1961 70551 116.9 554894 4007 2827 130081 1962 end reg y x1-x6 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[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