Creates a Continuous Uniform distribution object (Continuous Distribution).

If the probability density function of a random variable X is given by pf=[1/(b-a), x greater than or equal to a and x less than or equal to b ] 0 otherwise and the parameters a and b can take any number, however, with b has to be greater than a, then the random variable X is defined to be uniformally distributed over the interval [a, b]. The mean of a Continuous Uniform distribution is (a+b)/2, the variance is pow((b-a), 2)/12. The uniform distribution gets its name from the fact that its density is uniform over the interval [a, b]. It provides a useful model for a few random phenomena.

For instance, if it is known that the values of some random variable X can only be in a finite interval, say [a, b], and if one assumes that any two subintervals of [a, b] of equal length have the same probability of containing X, then X has a uniform distribution over the interval [a, b]. The string 'Key' resulting from a successful construction of this distribution object can be passed to the following functions in order to query (mean, std deviation and variance) or execute functions (probability function, cumulative density function etc...) based on this distribution object : CDistributionMean(), CDistributionVar(), CDistributionSTD(), CDistributionPDF(), CDistributionCDF(), CDistributionICDF() or CDistHazard(). In addition, the string 'Key' resulting from a successful construction of this distribution object will also allow you to construct a process generator object via a call to PGCUniformDistribution(). A process generator object allows you to generate large amounts of random numbers based on this distribution.

Even though PGCUniformDistribution() is the process generator object, the function RandomCUniform() is the actual function that obtains the random numbers given a count parameter and the process generator string 'key'.

This function creates an object and returns a string-key value to represent this created object.
The TAG value of the string-key returned (second part of the key) is : "CUniform"

String CTStatDistributions.CUniformDistribution( _
String Key, _
Long Reload, _
Double minval, _
Double maxval)

=CT.STAT.CUniformDistribution(
Excel String Cell Key,
Excel Numeric Cell Reload,
Excel Numeric Cell minval,
Excel Numeric Cell maxval)

static std::string CUniformDistribution(
std::string Key,
long Reload,
double minval,
double maxval);

System.String CTStatDistributionsSA.CUniformDistribution(
System.String Key,
System.Int32 Reload,
System.Double minval,
System.Double maxval);

The C# example below contains all the sub-function calls leading up to this function call. As a result, the example can contain a lot of code.

The VB.NET, J#, C++.NET, Java, Excel VBA, Visual Basic 6 (via COM) and C++ examples below contain function code stubs for the calls leading up to this function call. However, the function call for this function is displayed.
You can easily reproduce the stub functions code from the C# example.