Difference between revisions of "Student's t-distribution"

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[[category:Distribution Functions]]
 
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= StudentT(dof) =
 
   
 
   
 
The StudentT distribution.
 
The StudentT distribution.
  
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The Student-T distribution describes the deviation of a sample mean from the true mean when the samples are generated by a normally distributed process.  The statistic
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    t = ( m - u ) / (s * [[Sqrt]](n))
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where m is the sample mean, u the actual mean, s the sample standard deviation, and n the sample size, is distributed according to the Student-T distribution with n-1 degrees of freedom.  The parameter, «dof», is the degrees of freedom.  Student-T distributions are bell-shaped, much like a [[Normal|normal distribution]], but with heavier tails, especially for smaller degrees of freedom.  When n=1, it is known as the Cauchy distribution.  For efficiency reasons, when a latin-hypercube sampling method is selected, psuedo-latin-hypercube method is used to sample the Student-T, which samples from the T-distributiion, but does not guarantee a perfect latin spread of the samples.
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= Parameter Estimation =
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= See Also =
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* [[Dens_StudentT]]
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* [[CumStudentT]]

Revision as of 17:04, 5 August 2009


StudentT(dof)

The StudentT distribution.

The Student-T distribution describes the deviation of a sample mean from the true mean when the samples are generated by a normally distributed process. The statistic

   t = ( m - u ) / (s * Sqrt(n))

where m is the sample mean, u the actual mean, s the sample standard deviation, and n the sample size, is distributed according to the Student-T distribution with n-1 degrees of freedom. The parameter, «dof», is the degrees of freedom. Student-T distributions are bell-shaped, much like a normal distribution, but with heavier tails, especially for smaller degrees of freedom. When n=1, it is known as the Cauchy distribution. For efficiency reasons, when a latin-hypercube sampling method is selected, psuedo-latin-hypercube method is used to sample the Student-T, which samples from the T-distributiion, but does not guarantee a perfect latin spread of the samples.

Parameter Estimation

See Also

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