Here is the output from two regression models for overhead costs at a university using number of academic programs and number of students as potential cost drivers.
Number of academic programs
Adjusted R-square = 0.72
Intercept = 7,127.75 t-statistic = 2.14 p-value = .05
X1 variable = 240.64 t-statistic = 5.08 p-value = .001
Number of students
Adjusted R-square = 0.55
Intercept = 5,991.75 t-statistic = 1.18 p-value = .35
X1 variable = 3.78 t-statistic = 3.53 p-value = 0.01
a)Develop a cost function for each potential cost driver.
b)Compare the output for the two drivers. Choose the best cost driver for overhead costs and explain how you made that choice.
c)Suppose you use the best cost function from part (b)to estimate overhead cost for the next semester. Why is it highly unlikely that the actual cost will be exactly the same as the cost you estimated?

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The Answer of Here is the output from two regression models for overhead costs at a university using number of academic programs and number of students as potential cost drivers.
Number of academic programs
Adjusted R-square = 0.72
Intercept = 7,127.75 t-statistic = 2.14 p-value = .05
X1 variable = 240.64 t-statistic = 5.08 p-value = .001
Number of students
Adjusted R-square = 0.55
Intercept = 5,991.75 t-statistic = 1.18 p-value = .35
X1 variable = 3.78 t-statistic = 3.53 p-value = 0.01
a)Develop a cost function for each potential cost driver.
b)Compare the output for the two drivers. Choose the best cost driver for overhead costs and explain how you made that choice.
c)Suppose you use the best cost function from part (b)to estimate overhead cost for the next semester. Why is it highly unlikely that the actual cost will be exactly the same as the cost you estimated?

Here Is the Output from Two Regression Models for Overhead