___ ____ ____ ____ ____ (R)
/__ / ____/ / ____/
___/ / /___/ / /___/ 15.1 Copyright 1985-2017 StataCorp LLC
Statistics/Data Analysis StataCorp
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Serial number: 15
Licensed to: Kreshna Gopal
StataCorp LLC
Notes:
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running /home/krg/bin/profile.do ...
Compile number 629
. do wampler1.do
. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
>
> Linear Regression
>
> Difficulty=Higher Polynomial k=6 N=21 Generated
>
> Dataset Name: Wampler-1 (wampler1.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 0.000000000000000
> B1 1.00000000000000 0.000000000000000
> B2 1.00000000000000 0.000000000000000
> B3 1.00000000000000 0.000000000000000
> B4 1.00000000000000 0.000000000000000
> B5 1.00000000000000 0.000000000000000
>
> Residual
> Standard Deviation 0.000000000000000
>
> R-Squared 1.00000000000000
>
>
> Certified Analysis of Variance Table
>
> Source of Degrees of Sums of Mean
> Variation Freedom Squares Squares F Statistic
>
> Regression 5 18814317208116.7 3762863441623.33 Infinity
> Residual 15 0.000000000000000 0.000000000000000
> */
.
. clear
.
. scalar N = 21
. scalar df_r = 15
. scalar df_m = 5
.
. scalar rmse = 0
. scalar r2 = 1
. scalar mss = 18814317208116.7
. scalar F = .
. scalar rss = 0
.
. scalar b_cons = 1
. scalar se_cons = 0
. scalar bx1 = 1
. scalar sex1 = 0
. scalar bx2 = 1
. scalar sex2 = 0
. scalar bx3 = 1
. scalar sex3 = 0
. scalar bx4 = 1
. scalar sex4 = 0
. scalar bx5 = 1
. scalar sex5 = 0
.
. 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) = .
Model | 1.8814e+13 5 3.7629e+12 Prob > F = .
Residual | 0 15 0 R-squared = 1.0000
-------------+---------------------------------- Adj R-squared = 1.0000
Total | 1.8814e+13 20 9.4072e+11 Root MSE = 0
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .9999999 . . . . .
x2 | 1 . . . . .
x3 | 1 . . . . .
x4 | 1 . . . . .
x5 | 1 . . . . .
_cons | 1 . . . . .
------------------------------------------------------------------------------
. di "R-squared = " %20.15f e(r2)
R-squared = 1.000000000000000
.
. 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] 7.2
_b[x1] 6.9
_b[x2] 7.4
_b[x3] 8.3
_b[x4] 9.5
_b[x5] 11.2
-------------------------
min 6.9
_se[_cons] <exactly equal>
_se[x1] <exactly equal>
_se[x2] <exactly equal>
_se[x3] <exactly equal>
_se[x4] <exactly equal>
_se[x5] <exactly equal>
-------------------------
min 15.0
e(rmse) <exactly equal>
e(r2) <exactly equal>
e(mss) 14.7
e(F) <exactly equal>
e(rss) <exactly equal>
.
end of do-file