Data considerations for Contour Plot

To ensure that your results are valid, consider the following guidelines when you perform the analysis and interpret your results.

First, perform an analysis that fits and stores a model

Contour plots use a stored model that you fit for a response. If you did not perform an analysis that stores a model, you do not have a model to use. For more information, go to Stored model overview.

The model must contain at least two continuous variables

If the stored model does not contain at least two continuous variables, then this plot is not available. If you fit your model using Fit General Linear Model, then you can only include covariates on this plot. However, if you fit your model using either Analyze Factorial Design or Analyze Variability, then you can graph the continuous factors but not the covariates.

Note

A mixture design must have at least three component variables or at least two numeric process variables to create a plot.

Verify that the model meets the assumptions of the analysis

If the stored model does not meet the assumptions of the original analysis, the contour plot might be inaccurate. For more information, click the original analysis below, and then go to the "Data considerations" topic and the "Key results" topic.

• Analyze Factorial Design
• Analyze Binary Response for Factorial Design
• Analyze Definitive Screening Design
• Analyze Binary Response for Definitive Screening Design
• Analyze Variability
• Analyze Response Surface Design
• Analyze Binary Response for Response Surface Design
• Fit General Linear Model
• Fit Regression Model or Linear Regression
• Fit Binary Logistic Model or Binary Logistic Regression
• Fit Poisson Model
• Analyze Mixture Design

Variable settings should be similar to the data that you used to fit the model

If you use variable settings that are not within the range of the data that you used to fit the model, the fitted values can be misleading. Also, you should use combinations of variable settings that are similar to the combinations that you used to fit the model. If you identify points of interest, you can use Predict to determine whether these points are unusual compared to the data that you used to fit the model. Predict also includes prediction intervals in the output, which you can use to determine the precision of the predictions.

Note

To generate predictions for a mixture design, choose Stat > DOE > Mixture > Analyze Mixture Design > Prediction.