The data in the sample data table are the result of a designed experiment where the factors are orthogonal. For this reason, you use importance estimates based on independent inputs. Suppose that you believe that, in practice, factor values vary throughout the design space, rather than assume only the settings defined in the experiment. Then you should choose Independent Uniform Inputs as the sampling scheme for your importance indices.
Select Help > Sample Data Library and open
Run the script RSM for 4 Responses.
From the red triangle menu next to Prediction Profiler, select Assess Variable Importance > Independent Uniform Inputs.
The Summary Report is shown in Summary Report for Four Responses. Because the importance indices are based on random sampling, your estimates might differ slightly from those shown in the figure.
Summary Report for Four Responses
Profiler for Four Responses
The Marginal Model Plots report (Marginal Model Plots for Four Responses) shows mean responses for each factor across a uniform distribution of settings for the other two factors.
Marginal Model Plots for Four Responses

Help created on 9/19/2017