Standard stepwise regression is a good way of identifying potential multicollinearity problems since we are able to see the impact on the model at each step that occurs when a new variable is added to the model. For instance, if bringing in a new variable causes the sign to change on a previously entered variable, we have evidence of multicollinearity.

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Standard stepwise regression allows us to observe the changes in the model as each new variable is added, including any changes in the signs of previously entered variables, which can indicate multicollinearity.

Standard Stepwise Regression Is a Good Way of Identifying Potential