___ ____ ____ ____ ____ tm
/__ / ____/ / ____/
___/ / /___/ / /___/ 10.0 Copyright 1984-2007
Statistics/Data Analysis StataCorp
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College Station, Texas 77845 USA
800-STATA-PC http://www.stata.com
979-696-4600 stata@stata.com
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3-user Stata for Linux64 (network) perpetual license:
Serial number: 999
Licensed to: Brian P. Poi, PhD
StataCorp LP
Notes:
1. (-m# option or -set memory-) 1.00 MB allocated to data
2. Command line editing disabled
3. Stata running in batch mode
running /home/bpp/bin/profile.do ...
. do wampler2.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-2 (wampler2.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 0.100000000000000 0.000000000000000
> B2 0.100000000000000E-01 0.000000000000000
> B3 0.100000000000000E-02 0.000000000000000
> B4 0.100000000000000E-03 0.000000000000000
> B5 0.100000000000000E-04 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 6602.91858365167 1320.58371673033 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 = 6602.91858365167
. scalar F = .
. scalar rss = 0
.
. scalar b_cons = 1
. scalar se_cons = 0
. scalar bx1 = 1e-1
. scalar sex1 = 0
. scalar bx2 = 1e-2
. scalar sex2 = 0
. scalar bx3 = 1e-3
. scalar sex3 = 0
. scalar bx4 = 1e-4
. scalar sex4 = 0
. scalar bx5 = 1e-5
. scalar sex5 = 0
.
. qui input double 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 | 6602.91858 5 1320.58372 Prob > F = .
Residual | 0 15 0 R-squared = 1.0000
-------------+------------------------------ Adj R-squared = 1.0000
Total | 6602.91858 20 330.145929 Root MSE = 0
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .1 . . . . .
x2 | .01 . . . . .
x3 | .001 . . . . .
x4 | .0001 . . . . .
x5 | 1.00e-05 . . . . .
_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] 11.7
_b[x1] 10.4
_b[x2] 9.9
_b[x3] 9.7
_b[x4] 10.0
_b[x5] 10.7
-------------------------
min 9.7
_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) 15.4
e(F) <exactly equal>
e(rss) <exactly equal>
.
.
end of do-file