Difference between revisions of "Student's t-distribution"
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= StudentT(dof) = | = StudentT(dof) = | ||
− | The | + | 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 = | ||
+ | |||
+ | 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>: | ||
+ | :«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
:
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