Statistics/Normal distribution: Difference between revisions
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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. |
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# Mention any native language support for the generation of |
# Mention any native language support for the generation of normally distributed random numbers. |
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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
- 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 normally distributed random numbers.
- Reference
- You may refer to code in Statistics/Basic if available.