/* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ Linear Regression Difficulty=Average Linear k=1 N=11 Generated Dataset Name: Line Through Origin-1 (nointercept1.dat) Procedure: Linear Least Squares Regression Reference: Eberhardt, K., NIST. Data: 1 Response Variable (y) 1 Predictor Variable (x) 11 Observations Average Level of Difficulty Generated Data Model: Linear Class 1 Parameter (B1) y = B1*x + e Certified Regression Statistics Standard Deviation Parameter Estimate of Estimate B1 2.07438016528926 0.165289256198347E-01 Residual Standard Deviation 3.56753034006338 R-Squared 0.999365492298663 Certified Analysis of Variance Table Source of Degrees of Sums of Mean Variation Freedom Squares Squares F Statistic Regression 1 200457.727272727 200457.727272727 15750.2500000000 Residual 10 127.272727272727 12.7272727272727 */ clear scalar N = 11 scalar df_r = 10 scalar df_m = 1 scalar rmse = 3.56753034006338 scalar r2 = 0.999365492298663 scalar mss = 200457.727272727 scalar F = 15750.2500000000 scalar rss = 127.272727272727 scalar bx = 2.07438016528926 scalar sex = 0.165289256198347E-01 qui input int (y x) 130 60 131 61 132 62 133 63 134 64 135 65 136 66 137 67 138 68 139 69 140 70 end reg y x, nocons 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[x] bx () _se[x] sex () /* */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss