/* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ Linear Regression Difficulty=Lower Linear k=2 N=36 Observed Dataset Name: Norris (norris11.dat) Procedure: Linear Least Squares Regression Reference: Norris, J., NIST. Calibration of Ozone Monitors. Data: 1 Response Variable (y) 1 Predictor Variable (x) 36 Observations Lower Level of Difficulty Observed Data Model: Linear Class 2 Parameters (B0,B1) y = B0 + B1*x + e Certified Regression Statistics Standard Deviation Parameter Estimate of Estimate B0 -0.262323073774029 0.232818234301152 B1 1.00211681802045 0.429796848199937E-03 Residual Standard Deviation 0.884796396144373 R-Squared 0.999993745883712 Certified Analysis of Variance Table Source of Degrees of Sums of Mean Variation Freedom Squares Squares F Statistic Regression 1 4255954.13232369 4255954.13232369 5436385.54079785 Residual 34 26.6173985294224 0.782864662630069 */ clear scalar N = 36 scalar df_r = 34 scalar df_m = 1 scalar rmse = 0.884796396144373 scalar r2 = 0.999993745883712 scalar mss = 4255954.13232369 scalar F = 5436385.54079785 scalar rss = 26.6173985294224 scalar b_cons = -0.262323073774029 scalar se_cons = 0.232818234301152 scalar bx = 1.00211681802045 scalar sex = 0.429796848199937E-03 qui input double (y x) 0.1 0.2 338.8 337.4 118.1 118.2 888.0 884.6 9.2 10.1 228.1 226.5 668.5 666.3 998.5 996.3 449.1 448.6 778.9 777.0 559.2 558.2 0.3 0.4 0.1 0.6 778.1 775.5 668.8 666.9 339.3 338.0 448.9 447.5 10.8 11.6 557.7 556.0 228.3 228.1 998.0 995.8 888.8 887.6 119.6 120.2 0.3 0.3 0.6 0.3 557.6 556.8 339.3 339.1 888.0 887.2 998.5 999.0 778.9 779.0 10.2 11.1 117.6 118.3 228.9 229.2 668.4 669.1 449.2 448.9 0.2 0.5 end reg y x 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[x] bx () /* */ _se[_cons] se_cons _se[x] sex () /* */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss