Click the Linear Effects tab to review the linear
effects.

Observe the main effect of the input variables on both output responses.

Click the Pareto Plot tab, then use the
Channel selector to select both of the output
responses. Observe the results.

Note: A linear effects plot and a pareto plot with the Linear effects option
enabled (shown below) provide the same information. However, with a pareto
plot, you can use a statistical measure (that is, the 80-20 rule) to decide
which input variables are more significant and which input variables can be
neglected.

For this tutorial, you will use the 80/20 rule to eliminate
input variables that are not significant to the study. The 80/20 rule is a
Pareto principle that proposes 80% of the total effects comes from only 20% of
the variables.

Note: You should also use other practices to eliminate input
variables that you feel should be taken in consideration.

For screening purpose, you can see which input variables contribute to 80% or
more of the given output response. In the images below you can see the
following:

For Max_Acceleration, the input variables length_internal,
th_internal_skin, and th_external_skin contribute to 80% of the linear
effect.

For Max_Displacement, the input variables length_internal and
th_internal_skin fall under the 80/20 rule.

For n responses, you can list out the input variables that follow the 80/20
rule, and take union of the sets. In this case, the input variables that follow
the 80/20 rule include: length_internal, th_internal_skin, and th_external_skin.
This narrows your list to three significant input variables.