The importance of an input to an output is determined from their
correlation. The samples used for correlation calculation are generated
with the** Latin Hypercube Sampling** (LHS) method. The Latin Hypercube samples are generated in such
a way that the correlations among the input parameters are less than
or equal to 5%. Also, the Latin Hypercube samples are generated in
such a way that each sample is randomly generated, but *no two points share input parameters of the same value.*

The **Optimal Space-Filling Design** (OSF) method of sample generation is an LHS design that is extended
with postprocessing to achieve uniform space distribution of points,
maximizing the distance between points. The image below illustrates
how samples generated via the LHS method vary in placement from those
generated by the OSF sampling method.

The image below illustrates the generation of 20 samples via
the Monte Carlo, LHS, and OSF methods.

### Pearson’s Linear Correlation

Uses actual data for correlation evaluation.

Correlation coefficients are based on the sample values.

Used to correlate linear relationships.

### Spearman’s Rank Correlation

Uses ranks of data.

Correlation coefficients are based on the rank of
samples.

Recognizes non-linear **montonic** relationships (which are
less restrictive than linear ones). In a monotonic relationship, one
of the following two things happens:

As the value of one variable increases, the value
of the other variable increases as well.

As the value of one variable increases, the value
of the other variable decreases.

Deemed the more accurate method.