The Business School at Eastern College is collecting data as a first step in the preparation of next year€™s budget. One cost that is being looked at closely is administrative staff as a function of student credit hours. Data on administrative costs and credit hours for the most recent 13 months follow:

The controller€™s office has analyzed the data and given you the results from the regression analysis:

Required
a. In the standard regression equation y = a + bx, the letter b is best described as the
(1) Independent variable.
(2) Dependent variable.
(3) Constant coefficient.
(4) Correlation coefficient.
(5) Variable cost coefficient.
b. In the standard regression equation y = a + bx, the letter y is best described as the
(1) Independent variable.
(2) Correlation coefficient.
(3) Constant coefficient.
(4) Variable cost coefficient.
(5) Dependent variable.
c. In the standard regression equation y = a + bx, the letter x is best described as the
(1) Independent variable.
(2) Dependent variable.
(3) Constant coefficient.
(4) Variable cost coefficient.
(5) Correlation coefficient.
d. If the controller uses the high-low method to estimate costs, the cost equation for administrative costs is (numbers are rounded to the nearest dollar)
(1) Cost = $291,637 + 229 × Credit hours.
(2) Cost = $233,571 + 101 × Credit hours.
(3) Cost = $229.50 × Credit hours.
(4) Cost = $404,874.
(5) Some other equation.
e. Based on the results of the controller€™s regression analysis, the estimate of administrative costs in a month with 2,100 credit hours would be
(1) $834,993.
(2) $844,200.
(3) $404,917.
(4) $839,575.
(5) Some other amount.
f. The correlation coefficient (rounded) for the regression equation for administrative costs is
(1) 0.871.
(2) 0.933.
(3) 0.859.
(4) ˆš0.933.
(5) Some other amount.
g. The percent of the total variance (rounded) that can be explained by the regression is
(1) 85.9.
(2) 87.1.
(3) 93.3.
(4) 96.6.
(5) Some otheramount.

Transcribed Image Text:

Month Administrative Costs Credit Hours 5 S 543,064 346,975 960,036 908,855 1,084,705 774,686 920,375 1,029,000 880,496 806,085 1,049,908 715,756 538,301 $10,558,242 $ 812,172 July.... August September October. November December January. February. March. April. . May....- June.. -.- July 242 2,923 2,100 2,749 2,335 2,812 2,883 2,234 2,358 2,856 882 Total Average 25,561 1,966 1 SUMMARY OUTPUT Regression statisties 4 Multiple R 5 R Square 6 Adjusted R Square 0.859507749 0.871215438 7 Standard Error 83810.54742 8 Observations 10 ANOVA MS Signimcance F 12 Regression 13 Residual 14 Total 15.22699 11 5.227E 11 74.4133633 3,16774E-08 12 5.99985E+11 18 Coemcients Standard Emort Stat p-value | Lower g5% | Upper 95% 9.454BE-08 289042.6573 520706.4047 8.62835284 3.1677E-08 154.2933025 259.9993079 52827.2852 7.69324371 17 Interoept 18 Credt Hours 19 404874.531 5 207.1485552 240132254