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

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= StudentT(dof) =
 
= StudentT(dof) =
 
   
 
   
The StudentT distribution.
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The Student-T 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  
 
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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= Parameter Estimation =
 
= Parameter Estimation =
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If you want to estimate the parameter from sample data ''X'' indexed by ''I'', you can use the following estimation formula provided that <code>[[Variance]](X,I)>1</code>:
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:«dof» := 2 * [[Variance]](X,I) / ([[Variance]](X,I) - 1)
  
 
= See Also =
 
= See Also =

Revision as of 17:11, 5 August 2009


StudentT(dof)

The Student-T 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

If you want to estimate the parameter from sample data X indexed by I, you can use the following estimation formula provided that Variance(X,I)>1:

«dof» := 2 * Variance(X,I) / (Variance(X,I) - 1)

See Also

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