For each response, you need to define a low and a high contour. You should define contours that correspond to your goal for the responses. Here are some examples:
- If your goal is to minimize the response (smaller-is-better), you may want to set the Low value at the point of diminishing returns. The point of diminishing returns is the point at which going below a certain value does not make much difference. If there is no point of diminishing returns, use a very small number, one that is probably not achievable. Use your maximum acceptable value in High.
- If your goal is to target the response, you probably have upper and lower specification limits for the response. Because the graph displays the mean values for the response at the corresponding factor settings, you should set your low and high values well within your specification limits to ensure that individual observations fall with the specification limits. If you do not have specification limits, you may want to use lower and upper points of diminishing returns.
- If your goal is to maximize the response (larger-is-better), you may want to set the High value at the point of diminishing returns. The point of diminishing returns is the point at which going above a certain value does not make much difference. Use your minimum acceptable value in Low.
In all of these cases, the goal is to have the response fall between these two values.
- Response
- Lists the responses that you specified to display on the overlaid contour plot.
- Low
- Enter the low value for the contour lines for each response.
- High
- Enter the high value for the contour lines for each response.
Note
With a model from Fit Binary Logistic
Model or Analyze Binary
Response, the low and high values must be between 0 and 1.
