Example of Predict

A materials engineer at a furniture manufacturing site wants to assess the stiffness of the particle board that the manufacturer uses. The engineer collects stiffness data from particle board pieces that have various densities at different temperatures.

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

  1. Open the sample data, ParticleBoard.MTW.
  2. Open the Multiple Regression dialog box.
    • Mac: Statistics > Regression > Multiple Regression
    • PC: STATISTICS > Regression > Multiple Regression
  3. In Response, enter Stiffness.
  4. In Continuous predictors, enter Density and Temp, and then click OK.
    The model is now ready to use for prediction.
  5. Open the Predict dialog box.
    • Mac: Statistics > Regression > Predict
    • PC: STATISTICS > Regression > Predict
  6. In Response, select Stiffness.
  7. In the table, enter 16 for Density and 64 for Temp, and then click OK.

Interpret the results

The regression equation associated with the stored model is used to calculate the fitted, or predicted value for stiffness, which is 32.356. The prediction interval indicates that the engineer can be 95% confident that a single future value will fall within the range of 12.818 to 51.893. The prediction interval is so wide that the engineer cannot be confident that a new piece of particle board will be stiff enough.

Regression Equation
Settings
Variable
Setting
Prediction
Fit
SE Fit
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