First of all, Single Parameter Sensitivity (SPS) is different from correlation-based sensitivities, e.g., Pearson Linear (PLS) or Spearman Rank (SRS) Sensitivity used in ANSYS PDS and DX Six Sigma Analysis. SPS is more of a local sensitivity, while PLS or SRS is more of global sensitivity, that gives sensitivity value at a certain location in the design space of all parameters. For instance, for two input variables (X1 & X2), if we want to evaluate sensitivity of output Y wrt X1 at location (X1*,X2*), we evaluate the output Y of locations (points) along the axis X2 = X2*; X1 discretized at a certain interval between its specified bounds, while X2=X2* is kept throughout. The SPS is then formulated as follows: Ysps(X1 | X2*) = Ymax – Ymin ——-(1); subject to X1 discretization, and X2=X2* Likewise, Ysps(X2 | X1*) = Ymax – Ymin ——-(2); subject to X2 discretization, and X1=X1* Now, in order to have the same comparison basis, we want to normalize (1) and (2). What we do is normalize them with Yave, which is the average for all output Y evaluations of (1) and (2). The SPSs are then normalized as follows: Ysps(X1 | X2*) = (Ymax – Ymin) / Yave Ysps(X2 | X1*) = (Ymax – Ymin) / Yave