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Licensed to: Brian Poi
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Notes:
1. Command line editing disabled
2. Stata running in batch mode
running /home/bpp/bin/profile.do ...
. do longley.do
. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
>
> Linear Regression
>
> Difficulty=Higher Multilinear k=7 N=16 Observed
>
> Dataset Name: Longley (longley.dat)
>
> Procedure: Linear Least Squares Regression
>
> Reference: Longley, J. W. (1967).
> An Appraisal of Least Squares Programs for the
> Electronic Computer from the Viewpoint of the User.
> Journal of the American Statistical Association, 62, pp. 819-8
> 41.
>
> Data: 1 Response Variable (y)
> 6 Predictor Variable (x)
> 16 Observations
> Higher Level of Difficulty
> Observed Data
>
> Model: Polynomial Class
> 7 Parameters (B0,B1,...,B7)
>
> y = B0 + B1*x1 + B2*x2 + B3*x3 + B4*x4 + B5*x5 + B6*x6 + e
>
>
> Certified Regression Statistics
>
> Standard Deviation
> Parameter Estimate of Estimate
>
> B0 -3482258.63459582 890420.383607373
> B1 15.0618722713733 84.9149257747669
> B2 -0.358191792925910E-01 0.334910077722432E-01
> B3 -2.02022980381683 0.488399681651699
> B4 -1.03322686717359 0.214274163161675
> B5 -0.511041056535807E-01 0.226073200069370
> B6 1829.15146461355 455.478499142212
>
> Residual
> Standard Deviation 304.854073561965
>
> R-Squared 0.995479004577296
>
>
> Certified Analysis of Variance Table
>
> Source of Degrees of Sums of Mean
> Variation Freedom Squares Squares F Statistic
>
> Regression 6 184172401.944494 30695400.3240823 330.285339234588
> Residual 9 836424.055505915 92936.0061673238
> */
.
. clear
.
. scalar N = 16
. scalar df_r = 9
. scalar df_m = 6
.
. scalar rmse = 304.854073561965
. scalar r2 = 0.995479004577296
. scalar mss = 184172401.944494
. scalar F = 330.285339234588
. scalar rss = 836424.055505915
.
. scalar b_cons = -3482258.63459582
. scalar se_cons = 890420.383607373
. scalar bx1 = 15.0618722713733
. scalar sex1 = 84.9149257747669
. scalar bx2 = -0.358191792925910E-01
. scalar sex2 = 0.334910077722432E-01
. scalar bx3 = -2.02022980381683
. scalar sex3 = 0.488399681651699
. scalar bx4 = -1.03322686717359
. scalar sex4 = 0.214274163161675
. scalar bx5 = -0.511041056535807E-01
. scalar sex5 = 0.226073200069370
. scalar bx6 = 1829.15146461355
. scalar sex6 = 455.478499142212
.
. qui input double (y x1 x2 x3 x4 x5 x6)
.
. reg y x1-x6
Source | SS df MS Number of obs = 16
-------------+------------------------------ F( 6, 9) = 330.29
Model | 184172402 6 30695400.3 Prob > F = 0.0000
Residual | 836424.056 9 92936.0062 R-squared = 0.9955
-------------+------------------------------ Adj R-squared = 0.9925
Total | 185008826 15 12333921.7 Root MSE = 304.85
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 15.06187 84.91493 0.18 0.863 -177.029 207.1528
x2 | -.0358192 .033491 -1.07 0.313 -.1115811 .0399427
x3 | -2.02023 .4883997 -4.14 0.003 -3.125067 -.915393
x4 | -1.033227 .2142742 -4.82 0.001 -1.517949 -.548505
x5 | -.0511041 .2260732 -0.23 0.826 -.5625172 .460309
x6 | 1829.151 455.4785 4.02 0.003 798.7875 2859.515
_cons | -3482259 890420.4 -3.91 0.004 -5496529 -1467988
------------------------------------------------------------------------------
. di "R-squared = " %20.15f e(r2)
R-squared = 0.995479004577295
.
. 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[x4] bx4 _b[x5] bx5 _b[x6] bx6 () /*
> */ _se[_cons] se_cons _se[x1] sex1 _se[x2] sex2 /*
> */ _se[x3] sex3 _se[x4] sex4 _se[x4] sex4 _se[x5] sex5 _se[x6] sex6 () /*
> */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss
_b[_cons] 13.2
_b[x1] 12.3
_b[x2] 12.6
_b[x3] 13.3
_b[x4] 13.6
_b[x4] 13.6
_b[x5] 12.1
_b[x6] 13.2
-------------------------
min 12.1
_se[_cons] 13.0
_se[x1] 12.9
_se[x2] 12.9
_se[x3] 12.9
_se[x4] 13.2
_se[x4] 13.2
_se[x5] 12.9
_se[x6] 13.0
-------------------------
min 12.9
e(rmse) 13.5
e(r2) 15.2
e(mss) 15.8
e(F) 13.2
e(rss) 13.2
.
.
.
end of do-file