You can add interaction terms and polynomial terms to your model. By default, the model contains only the main effects for the predictor variables that you entered in the main dialog box. Click Default to return to this model at any time.
There are several ways to add terms. We use examples to illustrate them. For the examples, assume that the Predictors list has 3 continuous variables, X, Y, Z and 2 categorical variables, A, B.
- Add terms using selected predictors and model terms
- To add terms to the model, select at least one predictor or term. To select multiple items or to deselect an item, press the Ctrl key while you click the predictors or terms. When you add interactions and higher order terms, you increase the multicollinearity of the predictors. To reduce this source of multicollinearity, you can standardize the predictors. For more information go to Multicollinearity in regression.
- Interactions through order
- Add all interactions through the specified order. Suppose you select predictors X, Y, A and add interactions through order 3. When you click Add, Minitab adds X*Y, X*A, Y*A, X*Y*A.
- Terms through order
- Use to model curvature. This option adds powers and interactions through the specified order. Powers are for continuous predictors. Suppose you select X, Y, A and terms through order 3. When you click Add, Minitab adds the power terms for X and Y: X*X, Y*Y, X*X*X, Y*Y*Y. Minitab also adds interactions for the predictor variables and powers: X*Y, X*A, Y*A, X*X*Y, X*Y*Y, X*X*A, X*Y*A, Y*Y*A.
- Cross predictors and terms in the model
- This option can be used in the following ways:
- You can cross two or more predictors. Suppose you select X, Y, Z. When you click Add, Minitab adds the following terms: X*X, X*Y, X*Z.
- You can cross two or more terms that are already in the model. Suppose X*A and X*B are in the model. If you select only these terms and click Add, Minitab adds X*X*A*B.
- You can cross predictors with terms in the model. Suppose X*X and Y*Y are in the model. If you select these terms and predictors A, B, and then click Add, Minitab adds X*X*A, X*X*B, Y*Y*A, Y*Y*B. Each predictor is crossed with each model term, but the predictors are not crossed with themselves and the model terms are not crossed with themselves.
Note
You may need to deselect predictors or terms so that only the terms you want to cross are selected. To deselect items, press the Ctrl key while you click the predictors or terms.
- Terms in the model
- When you add terms to the model, the terms are listed in the white space in the dialog box. In this space, you can select individual terms or groups of terms to remove or reorder them.
- Default
- Populates the model with only the predictor variables that you entered in the main dialog box.
- Delete terms
- You can delete one or more terms from the model. Select the terms and click Delete (the red "X") in the dialog. You can also double-click a term to delete it.
- Reorder terms
- To move a term, select it, then click one of the arrow buttons in the dialog to move the term up or down. You can also move a contiguous block of terms. Click the first term then hold the Shift key and click the last term to select the whole block. Then click the appropriate arrow to move the block.
- Include the constant term in the model
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Select to include the constant term in the regression model. In most cases, you should include the constant in the model.
A possible reason to remove the constant is when you can assume that the response is 0 when the predictor values equal 0. For example, consider a model that predicts calories based on the fat, protein, and carbohydrate contents of a food. When the fat, protein, and carbohydrates are 0, the number of calories will also be 0 (or very close to 0).
When you compare models that do not include the constant, use S instead of the R2 statistics to evaluate the fit of models.
