Latin HyperCube
A square grid containing sample positions is a Latin square if, and only if, there is only one sample in each row and each column. A Latin HyperCube DOE, categorized as a space filling DOE, is the generalization of this concept to an arbitrary number of dimensions.
When sampling a design space of N variables, the range of each variable is divided
into M equally probable intervals. M sample points are then placed to satisfy the
Latin HyperCube requirements. As a result, all
experiments have unique levels for each input variable and the number of sample
points, M, is not a function of the number of input variables.
Usability Characteristics
 To get a good quality fitting function, a minimum number of runs should be evaluated. (N+1)(N+2)/2 runs are needed to fit a second order polynomial, assuming that most output responses are close to a second order polynomial within the commonly used input variable ranges of +10%. An additional number of runs equal to 10% is recommended to provide redundancy, which results in more reliable postprocessing. As a result, this equation is recommend to calculate the number of runs needed or a minimum of 1.1*(N+1)(N+2)/2 runs.
 The structure of a Latin HyperCube run matrix ensures that the runs are orthogonal. Orthogonality is desirable because it is less likely to result in singularities when creating Least Squares Regression fits.
 Any data in the inclusion matrix is combined with the run data for postprocessing. Any run matrix point which is already part of the inclusion data will not be rerun.
Settings
In the Specifications step, Settings tab, change method
settings.
Parameter  Default  Range  Description 

Number of Runs  $\frac{1.1(N+1)(N+2)}{2}$  > 0 integer  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.

Use Inclusion Matrix  Off  Off or On  Concatenation without duplication between the inclusion and the generated run matrix. 