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Of course as the chapter describes even if the

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Unformatted text preview: some correlation between the two independent variables, but multicollinearity does not appear to be a problem here. The significance of both independent variables (despite some correlation between them) suggests that each variable is a driver of overhead cost. Of course, as the chapter describes, even if the independent variables exhibited multicollinearity, Hanks should still prefer to use the multiple regression model over the simple regression models of Collaborative Learning Case 10‐28. Omitting any one of the variables will cause the estimated coefficient of the independent variable included in the model to be biased away from its true value. The Durbin‐Watson statistic = 1.91, so serial correlation in the residuals is not apparent. Caution: The sample size of 12 is small. Possible uses for the multiple regression results include: a. Planning and budgeting at Eastern University. The regression analysis indicates the variables (number of academic programs and number of enrolled students) that help predict changes in overhead costs. 10‐29 (co...
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