**A** longer run of the R code of yesterday with a million sudokus produced the following qqplot.

**I**t does look ok but no perfect. Actually, it looks very much like the graph of yesterday, although based on a 100-fold increase in the number of simulations. Now, if I test the adequation with a basic chi-square test (!), the result is highly negative:

> chisq.test(obs,p=pdiag/sum(pdiag)) #numerical error in pdiag

Chi-squared test for given probabilities

data: obs

X-squared = 6978.503, df = 6, p-value < 2.2e-16

(there are seven entries for both *obs* and *pdiag*, hence the six degrees of freedom). So this casts a doubt upon the uniformity of the random generator suggested in the paper by Newton and DeSalvo or rather on my programming abilities, see next post!

Filed under: R, Statistics Tagged: combinatorics, entropy, Kullback, Monte Carlo, simulation, sudoku, uniformity

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Xi'an's Og » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as: visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...

**Tags:** combinatorics, entropy, Kullback, Monte Carlo, R, Simulation, statistics, sudoku, uniformity