___ ____ ____ ____ ____ (R)
/__ / ____/ / ____/
___/ / /___/ / /___/ 11.0 Copyright 1984-2009
Statistics/Data Analysis StataCorp
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College Station, Texas 77845 USA
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3-user Stata network perpetual license:
Serial number: 999
Licensed to: Brian P. Poi
StataCorp LP
Notes:
1. (-m# option or -set memory-) 10.00 MB allocated to data
2. Command line editing disabled
3. Stata running in batch mode
running /home/bpp/bin/profile.do ...
. do norris.do
. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
>
> Linear Regression
>
> Difficulty=Lower Linear k=2 N=36 Observed
>
> Dataset Name: Norris (norris11.dat)
>
> Procedure: Linear Least Squares Regression
>
> Reference: Norris, J., NIST.
> Calibration of Ozone Monitors.
>
> Data: 1 Response Variable (y)
> 1 Predictor Variable (x)
> 36 Observations
> Lower Level of Difficulty
> Observed Data
>
> Model: Linear Class
> 2 Parameters (B0,B1)
>
> y = B0 + B1*x + e
>
>
> Certified Regression Statistics
>
> Standard Deviation
> Parameter Estimate of Estimate
>
> B0 -0.262323073774029 0.232818234301152
> B1 1.00211681802045 0.429796848199937E-03
>
> Residual
> Standard Deviation 0.884796396144373
>
> R-Squared 0.999993745883712
>
>
> Certified Analysis of Variance Table
>
> Source of Degrees of Sums of Mean
> Variation Freedom Squares Squares F Statistic
>
> Regression 1 4255954.13232369 4255954.13232369 5436385.54079785
> Residual 34 26.6173985294224 0.782864662630069
> */
.
. clear
.
. scalar N = 36
. scalar df_r = 34
. scalar df_m = 1
.
. scalar rmse = 0.884796396144373
. scalar r2 = 0.999993745883712
. scalar mss = 4255954.13232369
. scalar F = 5436385.54079785
. scalar rss = 26.6173985294224
.
. scalar b_cons = -0.262323073774029
. scalar se_cons = 0.232818234301152
. scalar bx = 1.00211681802045
. scalar sex = 0.429796848199937E-03
.
. qui input double (y x)
.
. reg y x
Source | SS df MS Number of obs = 36
-------------+------------------------------ F( 1, 34) = .
Model | 4255954.13 1 4255954.13 Prob > F = 0.0000
Residual | 26.6173985 34 .782864663 R-squared = 1.0000
-------------+------------------------------ Adj R-squared = 1.0000
Total | 4255980.75 35 121599.45 Root MSE = .8848
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | 1.002117 .0004298 2331.61 0.000 1.001243 1.00299
_cons | -.2623231 .2328182 -1.13 0.268 -.7354667 .2108205
------------------------------------------------------------------------------
. di "R-squared = " %20.15f e(r2)
R-squared = 0.999993745883712
.
. assert N == e(N)
. assert df_r == e(df_r)
. assert df_m == e(df_m)
.
. lrecomp _b[_cons] b_cons _b[x] bx () /*
> */ _se[_cons] se_cons _se[x] sex () /*
> */ e(rmse) rmse e(r2) r2 e(mss) mss e(F) F e(rss) rss
_b[_cons] 12.8
_b[x] 14.4
-------------------------
min 12.8
_se[_cons] 13.5
_se[x] 13.5
-------------------------
min 13.5
e(rmse) 13.5
e(r2) 15.5
e(mss) 15.2
e(F) 13.2
e(rss) 13.3
.
.
end of do-file