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Skip Navigation LinksHome»Documentation»Statistics Library User's Guide»Continuous Distributions»Student's t Distribution

Student's t Distribution

Extreme Optimization Numerical Libraries for .NET Professional

Student's t distribution is commonly used to test if the difference between the means of two samples is statistically significant. It is a variation of the normal distribution that takes into account that the mean of a sample is only an estimate for the mean of the population.

The Student t distribution has one shape parameter: the degrees of freedom, commonly denoted by the Greek letter ν. The probability density function is:

Probability density of Student's t distribution.

The Student t distribution is implemented by the StudentTDistribution class. It has one constructor with the degrees of freedom as its only argument.

The following constructs the Student t distribution with 8 degrees of freedom:

C#
VB
C++
F#
Copy
var studentT = new StudentTDistribution(8);
Dim studentT = New StudentTDistribution(8)

No code example is currently available or this language may not be supported.

let studentT = StudentTDistribution(8.0)

The StudentTDistribution class has one specific properties, DegreesOfFreedom, which returns the degrees of freedom of the distribution.

StudentTDistribution has one static (Shared in Visual Basic) method, Sample, which generates a random sample using a user-supplied uniform random number generator. The second and third parameters are the location and scale parameters of the distribution.

C#
VB
C++
F#
Copy
var random = new MersenneTwister();
double sample = StudentTDistribution.Sample(random, 8);
Dim random = New MersenneTwister()
Dim sample = StudentTDistribution.Sample(random, 8)

No code example is currently available or this language may not be supported.

let random = MersenneTwister()
let sample = StudentTDistribution.Sample(random, 8.0)

The above example uses the MersenneTwister class to generate uniform random numbers.

For details of the properties and methods common to all continuous distribution classes, see the topic on continuous distributions..

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