Alternatively hanks could run a multiple regression

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Unformatted text preview: those overhead costs that are more closely related to number of academic programs and (ii) those overhead costs more closely related to number of enrolled students, and rerun the simple regression analysis on each overhead cost pool. Alternatively, Hanks could run a multiple regression analysis with total overhead costs as the dependent variable and the number of academic programs and number of enrolled students as the two independent variables. Solution Exhibit 10‐29 evaluates the multiple regression model using the format of Exhibit 10‐14. Hanks should use the multiple regression model rather than the two simple regression models of Collaborative Learning Case 10‐28. The multiple regression model appears economically plausible and the regression model performs very well when estimating overhead costs. It has an excellent goodness of fit, significant t‐values on both independent variables, and meets all the specification assumptions for ordinary least squares regression. The adjusted R2 value = 0.766 is higher than the Collaborative Learning Case 10‐28 simple linear regression models R2 values. There is...
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This note was uploaded on 01/23/2014 for the course TELFER adm3346 taught by Professor Collier during the Winter '12 term at University of Ottawa.

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