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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