Use a contour plot to help you visualize the response surface. Contour plots are useful for establishing desirable response values, mixture blends, and operating conditions.

A contour plot shows how a response variable relates to three components based on a model equation. Points that have the same response are connected to produce contour lines of constant responses. Because a contour plot shows only three components at a time, while holding any other components and process variables at a constant level, the contour plots are only valid for fixed levels of the extra variables. If you change the holding levels, the response surface changes as well, sometimes drastically.

The display depends on whether the design contains process variables or an amount variable and how you choose to handle these variables. You can display a plot that shows all the levels of these extra variables (as shown here) or just display a single plot.

For the fondue data, the interpretation of the contour plots is as follows:

·    Both plots show how the component proportions are related to the flavor of the fondue. To maximize flavor, you would choose proportions for the components in the lower left corner of the design space where the flavor ratings are the highest. In both plots, the darkest green contour is the highest in the design space. The blend that produces the highest flavor rating is at the vertex that is comprised of:

30% Emmenthaler (p = 0.3), 30% Gruyere (p = 0.3), and 40% Wine (p = 0.4)

The proportions of the components must be selected in such a manner that they sum to one.

·    Compare the plots where serving temperature is 80° versus 90° to see which level of the process variable results in better flavor. The flavor ratings in the lower left corner of the design space are higher when the serving temperature is 90° than when it is 80°.