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running /home/krg/bin/profile.do ...
Compile number 629

. do noint2.do 

. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
> 
> Linear Regression
> 
> Difficulty=Average  Linear k=1  N=3  Generated
> 
> Dataset Name:  Line Through Origin-2 (nointercept2.dat)
> 
> Procedure:     Linear Least Squares Regression
> 
> Reference:     Eberhardt, K., NIST.
> 
> Data:          1 Response Variable (y)
>                1 Predictor Variable (x)
>                3 Observations
>                Average Level of Difficulty
>                Generated Data
> 
> Model:         Linear Class
>                1 Parameter (B1)
> 
>                y = B1*x + e
> 
> 
>                Certified Regression Statistics
> 
>                                           Standard Deviation
>      Parameter          Estimate             of Estimate
> 
>         B1          0.727272727272727    0.420827318078432E-01
> 
>      Residual
>      Standard Deviation   0.369274472937998
> 
>      R-Squared            0.993348115299335
> 
> 
>                Certified Analysis of Variance Table
> 
> Source of Degrees of    Sums of             Mean
> Variation  Freedom      Squares            Squares           F Statistic
> 
> Regression   1     40.7272727272727    40.7272727272727   298.6666666666667
> Residual     2     0.272727272727273   0.136363636363636
> */
. 
. clear

. 
. scalar N        = 3

. scalar df_r     = 2

. scalar df_m     = 1

. 
. scalar rmse     = 0.369274472937998

. scalar r2       = 0.993348115299335

. scalar mss      = 40.7272727272727

. scalar F        = 298.6666666666667

. scalar rss      = 0.272727272727273

. 
. scalar bx       = 0.727272727272727

. scalar sex      = 0.420827318078432E-01

. 
. qui input int (y x)

. 
. reg y x, nocons

      Source |       SS           df       MS      Number of obs   =         3
-------------+----------------------------------   F(1, 2)         =    298.67
       Model |  40.7272727         1  40.7272727   Prob > F        =    0.0033
    Residual |  .272727273         2  .136363636   R-squared       =    0.9933
-------------+----------------------------------   Adj R-squared   =    0.9900
       Total |          41         3  13.6666667   Root MSE        =    .36927

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .7272727   .0420827    17.28   0.003     .5462053    .9083401
------------------------------------------------------------------------------

. di "R-squared = " %20.15f e(r2)
R-squared =    0.993348115299335

. 
. assert N    == e(N)

. assert df_r == e(df_r)

. assert df_m == e(df_m)

. 
. lrecomp _b[x] bx () _se[x] sex () /*
> */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss

_b[x]               15.2
-------------------------
min                 15.2

_se[x]              15.2
-------------------------
min                 15.2

e(rmse)             15.8
e(r2)               16.0
e(mss)              15.2
e(F)                15.1
e(rss)              14.7

. 
end of do-file