Interpret the key results for 3D Scatterplot

Use a 3D scatterplot to see how a response variable relates to two predictor variables. A 3D scatterplot is a three-dimensional graph that is useful for investigating desirable response values and operating conditions.

A 3D scatterplot contains the following elements:
  • Predictor values on the x- and y-axes.
  • Response values on the z-axis.
Using the 3D scatterplot, you can view only the actual data values, with no interpolation between the data points such as in a contour plot or 3D surface plot. Use the plot to explore the direction, strength, and linearity of the relationship between the three variables:
  • Data points that tend to rise together suggest a positive correlation.
  • Data points that tend to rise as other data points tend to decline suggests a negative correlation.
  • Outliers fall far from the main group of data points.

Adding project lines helps you visualize each point's position in three-dimensional space. For more information, go to Adding project lines to a graph. Rotate the graph to view the plot from different angles and explore possible relationships in the data. For more information, go to Viewing a 3D scatterplot.

Key Results: 3D Scatterplot

This 3D scatterplot shows the relationship between the time and temperature settings used to cook a frozen dinner and the quality score assigned by food testers. Heating the dinner at shorter time intervals results in under-cooked product and low quality scores. However, heating at the longest intervals combined with the highest temperatures also results in low scores because the food becomes over-cooked. The optimal settings appear to be between 400° and 450° and between approximately 30 and 36 minutes.

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