Conducting stepwise regression


  1. Select Stat >> Regression >> Stepwise...
  2. In the box labeled Response, specify the response.
  3. In the box labeled Predictors, specify all the predictors that you want considered for the model (including the ones that appear in the following Predictors to include in every model box).
  4. (Optional) In the box labeled Predictors to include in every model, specify all of the predictors that must be included in every model considered.
  5. (Optional) Under Methods..., specify the Alpha to enter and Alpha to remove significance levels. The default for both is 0.15. Select OK.
  6. Select OK. The output will appear in the session window.


Researchers were interested in learning how the composition of cement affected the heat evolved during the hardening of the cement. Therefore, they measured and recorded the following data (cement.txt) on 13 batches of cement:

  • Response y: heat evolved in calories during hardening of cement on a per gram basis
  • Predictor x1: % of tricalcium aluminate
  • Predictor x2: % of tricalcium silicate
  • Predictor x3: % of tetracalcium alumino ferrite
  • Predictor x4: % of dicalcium silicate

Perform stepwise regression on the data set. Let αE = αR = 0.15. In doing so, require that the predictor x2 be included in all models considered.

Minitab dialog boxes

Sample output

minitab output

Close this window when finished.

© 2004 The Pennsylvania State University. All rights reserved.
Materials developed by Dr. Laura J. Simon (Lecturer, Penn State Department of Statistics).