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running /home/krg/bin/profile.do ...
Compile number 785
. do wampler4.do
. /* NIST/ITL StRD benchmark
>
> 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. 549-5
> 65.
>
> 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 67.552445824012
> 2
> 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
.
. gen int x2 = x1*x1
. gen long x3 = x1*x2
. gen long x4 = x1*x3
. gen long x5 = x1*x4
.
. reg y x1-x5
Source | SS df MS Number of obs = 21
-------------+---------------------------------- F(5, 15) = 67.55
Model | 1.8814e+13 5 3.7629e+12 Prob > F = 0.0000
Residual | 8.3554e+11 15 5.5703e+10 R-squared = 0.9575
-------------+---------------------------------- Adj R-squared = 0.9433
Total | 1.9650e+13 20 9.8249e+11 Root MSE = 2.4e+05
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .9999997 236355.2 0.00 1.000 -503778.1 503780.1
x2 | 1 77934.35 0.00 1.000 -166112.1 166114.1
x3 | 1 10147.55 0.00 1.000 -21627.99 21629.99
x4 | 1 564.5665 0.00 0.999 -1202.345 1204.345
x5 | 1 11.23249 0.09 0.930 -22.94148 24.94148
_cons | 1 215232.6 0.00 1.000 -458756.5 458758.5
------------------------------------------------------------------------------
. di "R-squared = " %20.15f e(r2)
R-squared = 0.957478440825662
.
. 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
_b[_cons] 6.8
_b[x1] 6.5
_b[x2] 6.9
_b[x3] 7.7
_b[x4] 9.0
_b[x5] 10.7
-------------------------
min 6.5
_se[_cons] 11.4
_se[x1] 10.9
_se[x2] 10.8
_se[x3] 10.8
_se[x4] 10.8
_se[x5] 10.8
-------------------------
min 10.8
e(rmse) 14.8
e(r2) 15.9
e(mss) 14.7
e(F) 15.2
e(rss) <exactly equal>
.
end of do-file