Latin HyperCube
An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling.
The Latin HyperCube method uses a constrained or stratified sampling scheme.
Latin HyperCube sampling selects
$n$
different values from each of
$k$
variables
$x1$
, …
$xk$
in the following manner:
 The range of each random variable is divided into $n$ nonoverlapping intervals on the basis of equal probability.
 One value from each interval is selected at random with respect to the probability density in the interval.
 The $n$ values thus obtained for $x1$ are paired in a random manner with the $n$ values of $x2$ . These $n$ pairs are combined in a random manner with the $n$ values of $x3$ to form $n$ triplets and so on, until $n$ ktuplets are formed.
Usability Characteristics
 A stratified sampling scheme like Latin HyperCube offers the advantage of selecting random variable values that are uniformly spread across the range of random variables while taking into account the probability density function of those random variables.
 A correlation structure can be specified to reflect the correlation existing between random variables. Applying a correlation structure can be costly for a large number of input variables.
Settings
In the Specifications step, Settings tab, change method
settings.
Parameter  Default  Range  Description 

Number of Runs  100  > 0  Number of new designs to be evaluated. 
Random Seed  1  Integer 0 to 10000 
Controlling repeatability of
runs depending on the way the sequence of random numbers is
generated.

Apply User Correlations  On  Off or On  Apply user specified correlations on the data. 