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running /home/krg/bin/profile.do ...
Compile number 785
. do sl8b.do
.
. /* NIST/ITL StRD benchmark
> *** 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 double-precision 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=201 k=9 Generated
>
> Dataset Name: Simon-Lesage8 (Simon-Lesage8.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. 325-332.
>
>
> Data: 1 Factor
> 9 Treatments
> 201 Replicates/Cell
> 1809 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 1.60800000000000E+01 2.01000000000000E+00
> 2.01000000000000E+02
> Within Treatment 1800 1.80000000000000E+01 1.00000000000000E-02
>
> Certified R-Squared 4.71830985915493E-01
>
> Certified Residual
> Standard Deviation 1.00000000000000E-01
> */
.
. clear
.
. scalar N = 1809
. scalar df_r = 1800
. scalar df_m = 8
.
. scalar mss = 1608
. scalar F = 201
. scalar rss = 1800
. scalar r2 = 4.71830985915493E-01
. scalar rmse = 1
.
. qui input byte treat double resp
.
. anova resp treat
Number of obs = 1,809 R-squared = 0.4718
Root MSE = 1 Adj R-squared = 0.4695
Source | Partial SS df MS F Prob>F
-----------+----------------------------------------------------
Model | 1608 8 201 201.00 0.0000
|
treat | 1608 8 201 201.00 0.0000
|
Residual | 1800 1,800 1
-----------+----------------------------------------------------
Total | 3408 1,808 1.8849558
.
. 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) <exactly equal>
e(rmse) <exactly equal>
e(r2) <exactly equal>
e(mss) <exactly equal>
e(rss) <exactly equal>
.
end of do-file