Example of Predict with a Poisson regression model

A quality engineer is concerned about discolored streaks in molded resin parts. Discolored streaks in the final product can be produced by contamination in hoses, and higher temperatures. The engineer identifies three possible predictor variables for the responses (defects). The engineer records the number of defect observed in hour long sessions, while varying the predictor levels.

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 calculates a prediction interval to determine a range of likely values for future observations at specified settings.

  1. Open the sample data, ResinDefects.MTW.
  2. Choose Stat > Regression > Poisson Regression > Predict.
  3. From Response, select Discoloration Defects.
  4. In the table, enter 6 for Hours Since Cleanse, 115 for Temperature, and large for Size of Screw.
  5. Click OK.

Interpret the results

Minitab uses the stored model to calculate that the predicted number of discoloration defects is 72.1682. The prediction interval indicates that the engineer can be 95% confident that the mean number of discoloration defects will fall within the range of 67.5477 to 77.1047.

Prediction for Discoloration Defects

Regression Equation Discoloration Defects = exp(Y')

Y' = 4.3982 + 0.01798 Hours Since Cleanse - 0.001974 Temperature + 0.000000 Size of Screw_large - 0.1546 Size of Screw_small

Settings Variable Setting Hours Since Cleanse 6 Temperature 115 Size of Screw large
Prediction Fit SE Fit 95% CI 72.1682 2.43628 (67.5477, 77.1047)
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