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probabilistic Design analysis • 105
3.5.2.2 Scatter Plots

While the sensitivities point indicate which probabilistic design param-
eters you need to modify to have an impact on the reliability or failure
probability, scatter plots give you a better understanding of how and how
far you should modify the input variables. Improving the reliability and
quality of a product typically means that the scatter of the relevant RPs
must be reduced.
The PDS allows you to request a scatter plot of any probabilistic
design variable versus any other one, so you can visualize the relation-
ship between two design variables (input variables or output parameters).
This allows you to verify that the sample points really show the pattern of
correlation that you specified (if you did so). Typically, RPs are correlated
because they are generated by the same set of RVs. To support the process
of improving the reliability or quality of your product, a scatter plot show-
ing a random output parameter as a function of the most important random
input variable can be very helpful.
When you display a scatter plot, the PDS plots the sampling points
and a trendline. For this trendline, the PDS uses a polynomial function
and lets you choose the order of the polynomial function. If you plot a
random output parameter as a function of a random input variable, then
this trendline expresses how much of the scatter on the random output
parameter (Y-axis) is controlled by the random input variable (X-axis).
The deviations of the sample points from the trendline are caused and
controlled by all the other RVs. If you want to reduce the scatter of the
random output parameter to improve reliability and quality, you have
two options:

• Reduce the width of the scatter of the most important random input
variable(s) (that you have control over).
• Shift the range of the scatter of the most important random input
variable(s) (that you have control over).

The effect of reducing and shifting the scatter of a random input vari-
able is illustrated in the following figures. “Input range before” denotes
the scatter range of the random input variable before the reduction or shift-
ing, and “input range after” illustrates how the scatter range of the random
input variable has been modified. In both cases, the trendline tells how
much the scatter of the output parameter is affected and in which way the
range of scatter of the random input variable is modified.