Minitab® – Procedure
- Select Stat >> Regression >> Regression >> Fit Regression Model...
- In the box labeled Response, specify the response.
- In the box labeled Continuous Predictors, specify all the predictors that you want to be considered for the model.
- Click on the Stepwise button.
- Choose 'Stepwise' from among the Method pull-down options.
- (Optional) Use the buttons below the box labeled Potential terms to indicate terms to include in every model, specify all of the predictors that must be included in every model considered.
- (Optional) Specify the Alpha to enter and Alpha to remove significance levels. The default for both is 0.15.
- Check the box labeled 'Display the table of model selection details using the pull-down to select 'Include details for each step'.
- Select OK.
- 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
Regression analysis: y versus x1, x2, x3, x4
Stepwise Selection of Terms
Candidate terms: x1, x2, x3, x4
|Terms||--------Step 1--------||--------Step 2--------|
\(\alpha\) to enter = 0.15, \(\alpha\) to remove = 0.15 At your request, the stepwise procedure included these terms in every module: x2