After regression, how can I do calculation using r2 and N?

[Yao Zhao]

After regression, I want to calculate the scalar, that is r2*N

How should I do that? I know my bad method is that copy and paste numbers from the result window, but since the numbers shown on the screen are truncated this method is bad.

Many thanks in advance!

[FernandoRios]

Perhaps this:

sysuse auto, clear
(1978 Automobile Data)

. reg price mpg

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(1, 72)        =     20.26
       Model |   139449474         1   139449474   Prob > F        =    0.0000
    Residual |   495615923        72  6883554.48   R-squared       =    0.2196
-------------+----------------------------------   Adj R-squared   =    0.2087
       Total |   635065396        73  8699525.97   Root MSE        =    2623.7

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |  -238.8943   53.07669    -4.50   0.000    -344.7008   -133.0879
       _cons |   11253.06   1170.813     9.61   0.000     8919.088    13587.03
------------------------------------------------------------------------------

. ereturn list

scalars:
                  e(N) =  74
               e(df_m) =  1
               e(df_r) =  72
                  e(F) =  20.25835256291883
                 e(r2) =  .2195828561874974
               e(rmse) =  2623.652888667586
                e(mss) =  139449473.5462301
                e(rss) =  495615922.5753915
               e(r2_a) =  .2087437291901015
                 e(ll) =  -686.5395809065244
               e(ll_0) =  -695.7128688987767
               e(rank) =  2

macros:
            e(cmdline) : "regress price mpg"
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "price"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"

matrices:
                  e(b) :  1 x 2
                  e(V) :  2 x 2

functions:
             e(sample)   

. display e(N)*e(r2)
16.249131