Difference between revisions of "Student's t-distribution"
m (adding doc status category stub page) |
|||
Line 1: | Line 1: | ||
[[category:Distribution Functions]] | [[category:Distribution Functions]] | ||
[[Category:Doc Status D]] <!-- For Lumina use, do not change --> | [[Category:Doc Status D]] <!-- For Lumina use, do not change --> | ||
+ | |||
+ | = StudentT(dof) = | ||
The StudentT distribution. | 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|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 = | ||
+ | |||
+ | * [[Dens_StudentT]] | ||
+ | * [[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.
Enable comment auto-refresher