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