Difference between revisions of "Student's t-distribution"
m (Lchrisman moved page Student's t- distribution to Student's t-distribution without leaving a redirect: extra space in the name) |
m (categories) |
||
Line 1: | Line 1: | ||
− | [[category: | + | [[category:Continuous distributions]] |
+ | [[category:Unbounded distributions]] | ||
+ | [[category:Unimodal distributions]] | ||
+ | [[category:Symmetric distributions]] | ||
+ | [[category:Univariate distributions]] | ||
== StudentT(dof) == | == StudentT(dof) == |
Revision as of 01:41, 29 September 2018
StudentT(dof)
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-distribution, 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)
Enable comment auto-refresher