I'm in the process of putting together some slides on how to apply Quantile-Quantile plots to performance data. Q-Q plots are a handy tool for visually inspecting how well your data matches a known probability distribution (prob dsn). If the match is good, the data should line up more or less diagonally in the Q-Q plot. A common usage is to verify normality, i.e. how well the data matches a Normal or Gaussian dsn. In fact, this usage is so common that R even has a separate function called qqnorm() for doing just that.
Showing posts with label Zipf's law. Show all posts
Showing posts with label Zipf's law. Show all posts
Wednesday, August 3, 2011
Tuesday, December 2, 2008
Linux Meets Zipf's Law
It's amazing how power laws seem to be seen in everything these days:
It's also amazing what passes for physics these days:
A team of researchers from ETH Zürich (Einstein's alma mater) in Switzerland used four decades of data detailing the evolution of open source software applications created for a Linux operating system to confirm the adherence to Zipf's law. The team studied Debian Linux, as it is continuously being developed by more than 1,000 volunteers from around the world. Beginning with 474 packages in 1996, Debian has expanded to include more than 18,000 packages today. The packages form an intricate network, with some packages having greater connectivity than others, as defined by how many other packages depend on a given package.
It's also amazing what passes for physics these days:
T. Maillart.; D. Sornette; S. Spaeth, and G. von Krogh. “Empircal Tests of Zipf’s Law Mechanism in Open Source Linux Distribution.” Physical Review Letters 101 218701 (2008).
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