Simple Random
The conventional approach of sampling is commonly called Simple Random or Monte Carlo. In Simple Random sampling, a pseudorandom number generator is used for generating random numbers from 0 to 1.
Design points are generated by using the Inverse Transform method. Clustering may
occur in the design point distribution because the sequence of samples is
random.
Usability Characteristics
 The statistical measures (such as mean or standard deviation) of a random sample group requires large numbers of runs to converge the given probability distributionâ€™s statistical measures.
 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. 