___ ____ ____ ____ ____ tm
/__ / ____/ / ____/
___/ / /___/ / /___/ 9.0 Copyright 19842005
Statistics/Data Analysis StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800STATAPC http://www.stata.com
9796964600 stata@stata.com
9796964601 (fax)
3user Stata for Linux64 (network) perpetual license:
Serial number: 999
Licensed to: Brian P. Poi, Ph.D.
StataCorp LP
Notes:
1. (m# option or set memory) 1.00 MB allocated to data
2. Command line editing disabled
3. Stata running in batch mode
running /home/bpp/bin/profile.do ...
. do sl9b.do
.
. /* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/
> *** MODIFIED by William Gould, StataCorp.
> ***
> *** The dependent variable in the original test had values such as
> *** as 1000000000000.4, 1000000000000.3, etc. These numbers cannot be
> *** stored on a binary computer with more than 4 digits of accuracy.
> *** E.g., in double precision,
> ***
> *** 1000000000000.4  1000000000000 = .40002441...
> ***
> *** That is, you might enter 1000000000000.4, but a doubleprecision binar
> y
> *** computer actually stores the number 1000000000000.40002441...
> ***
> *** Thus, even a perfectly accurate ANOVA routine could not obtain the
> *** the results the authors intended because, at the instant the data was
> *** stored, numbers were rounded. The test, as given, amounts to a test
> *** of whether data is being stored in better than binary double precision
> .
> ***
> *** The intention of the test, I believe, was to determine if the ANOVA
> *** routine could deal with numbers that varied only in their trailing
> *** digits.
> ***
> *** Thus, the test is modified as follows:
> ***
> *** 1) y is multiplied by 10. What was previously 1000000000000.4
> *** now becomes 10000000000004, a number that a digital computer
> *** can store accurately in double precision.
> ***
> *** 2) All results should now be as the authors originally expect
> *** except sums of squares of y will be multiplied by 100 and
> *** the root mean square error will be mutliplied by 10.
> ***
> *** The remaining comments below, from the NIST data, are not modified.
>
>
> ANOVA
>
> Difficulty=Higher n_i=2001 k=9 Generated
>
> Dataset Name: SimonLesage9 (SimonLesage9.dat)
>
>
> Procedure: Analysis of Variance
>
>
> Reference: Simon, Stephen D. and Lesage, James P. (1989).
> "Assessing the Accuracy of ANOVA Calculations in
> Statistical Software".
> Computational Statistics & Data Analysis, 8, pp. 325332.
>
>
> Data: 1 Factor
> 9 Treatments
> 2001 Replicates/Cell
> 18009 Observations
> 13 Constant Leading Digits
> Higher Level of Difficulty
> Generated Data
>
>
> Model: 10 Parameters (mu,tau_1, ... , tau_9)
> y_{ij} = mu + tau_i + epsilon_{ij}
>
>
> Certified Values:
>
> Source of Sums of Mean
> Variation df Squares Squares F Statis
> tic
>
> Between Treatment 8 1.60080000000000E+02 2.00100000000000E+01
> 2.00100000000000E+03
> Within Treatment 18000 1.80000000000000E+02 1.00000000000000E02
>
> Certified RSquared 4.70712773465067E01
>
> Certified Residual
> Standard Deviation 1.00000000000000E01
> */
.
. clear
.
. scalar N = 18009
. scalar df_r = 18000
. scalar df_m = 8
.
. scalar mss = 16008
. scalar F = 2001
. scalar rss = 18000
. scalar r2 = 4.70712773465067E01
. scalar rmse = 1
.
. qui input byte treat double resp
.
.
. anova resp treat
Number of obs = 18009 Rsquared = 0.4707
Root MSE = 1 Adj Rsquared = 0.4705
Source  Partial SS df MS F Prob > F
+
Model  16008 8 2001 2001.00 0.0000

treat  16008 8 2001 2001.00 0.0000

Residual  18000 18000 1
+
Total  34008 18008 1.888494
.
. assert N == e(N)
. assert df_r == e(df_r)
. assert df_m == e(df_m)
.
. lrecomp e(F) F e(rmse) rmse e(r2) r2 e(mss) mss e(rss) rss
e(F)
e(rmse)
e(r2)
e(mss)
e(rss)
.
end of dofile