# Student's t-distribution

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The Student's t-distribution describes the deviation of a sample mean from the true mean when the samples are generated by a normally distributed process. It is a continuous, unbounded, symmetric and unimodal distribution.

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's t-distribution with *n - 1* degrees of freedom. The parameter, «dof», is the degrees of freedom. Student's 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.

## Contents

## Functions

### StudentT( dof*, over* )

The distribution function. Use this to specify that a chance variable or uncertain quantity has a Student's t-distribution with «dof» degrees of freedom.

Use the optional «over» parameter to create independent and identically distributed quantities over one or more indexes.

### DensStudentT(x, dof*, over*)

*, over*)

The probability density at «x», given by

- $ p(x) = { {\Gamma\left({ {d+1}\over 2}\right)}\over {\sqrt{\pi d} \Gamma\left( d\over 2 \right) }} \left( 1 + { x^2 \over d} \right)^{-{ {d+1}\over 2} } $

where $ d $ is «dof», and $ \Gamma(x) $ is GammaFn.

### CumStudentT(x, dof*, over*)

*, over*)

The cumulative density up to «x», i.e., the probability that the outcome is less than or equal to «x».

- $ F(x) = { {\Gamma\left({ {d+1}\over 2}\right)}\over {\sqrt{\pi d} \Gamma\left( d\over 2 \right) }} \int_{\infty}^x \left( 1 + { t^2 \over d} \right)^{-{ {d+1}\over 2} } dt $

### CumStudentTInv(p, dof*, over*)

*, over*)

The inverse cumulative probability function, aka quantile function. This is value x at which the area under the probability density graph falling at or to the left of x is «p».

## Statistics

When 0<dof<=1, all moments are undefined.

The theoretical statistics (i.e., in the absence of sampling error) when dof>1 are as follows.

- Mean = Mode = Median = 0
- Variance = $ \left\{\begin{array}{ll} \infty & \mbox{when } 1 < dof \leq 2 \\ dof / (dof-2) & \mbox{when } dof>2\end{array}\right. $
- Skewness = 0, when dof>3.
- Kurtosis = $ \left\{\begin{array}{ll} \infty & \mbox{when } 2 < dof \leq 4 \\ 6 / (dof-4) & \mbox{when } dof>4\end{array}\right. $

## 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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