The engineer wants to study how several predictors affect discoloration defects in resin parts. Because the response variable describes the number of times that an event occurs in a finite observation space, the engineer fits a Poisson model.
The engineer fits the Poisson model and uses a surface plot to illustrate the relationship between the fitted values of discoloration defects and the settings for the hours since the last cleanse and the transfer temperature.
- Open the sample data, ResinDefects.MTW.
- Choose .
- From Response, select Discoloration Defects.
- Under Select a pair of variables for a single plot, select Temperature from X Axis and select Hours Since Cleanse from Y Axis.
- Click OK.
Interpret the results
Minitab uses the stored model to create the surface plot. The lowest number defects are in the lower right corner of the plot, which corresponds with higher temperatures and shorter times since that last cleanse. The third predictor, Size of Screw, is a categorical predictor and is not displayed in the plot. Minitab holds the value of screw size at "large" when calculating the fitted response values of the number of defects.
After assessing this plot, the analyst can change screw size from "large" to "small" and compare the number of defects on the new plot.
