Statistics/Normal distribution: Difference between revisions

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m (Mention Gaussian. expand task to include mention of native support. - Just ideas for the draft.)
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# Take a uniform random number generator and create a large (you decide how large) set of numbers that follow a normal (Gaussian) distribution. Calculate the dataset's mean and stddev, and show the histogram here.
# Take a uniform random number generator and create a large (you decide how large) set of numbers that follow a normal (Gaussian) distribution. Calculate the dataset's mean and stddev, and show the histogram here.
# Mention any native language support for the generation of normal distributed random numbers.
# Mention any native language support for the generation of normally distributed random numbers.


;Reference:
;Reference:

Revision as of 11:12, 27 July 2011

Statistics/Normal distribution is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

Normal (or Gaussian) distribution is a freqently used distribution in statistics. While most programming languages provide a uniformly distributed random number generator, one can derive normally distributed random numbers from a uniform generator.

The task
  1. Take a uniform random number generator and create a large (you decide how large) set of numbers that follow a normal (Gaussian) distribution. Calculate the dataset's mean and stddev, and show the histogram here.
  2. Mention any native language support for the generation of normally distributed random numbers.
Reference