Sandy Dawson is the Chief Operating Officer at Mercy Hospital in Atlanta, Georgia. She is analyzing the hospital's overhead costs but is not sure whether nursing hours or the number of patient days would be the best cost driver to use for predicting the hospital's overhead. She has gathered the following information for the last six months of the most recent year:

Requirements
1. Are the hospital's overhead costs fixed, variable, or mixed? Explain.
2. Graph the hospital's overhead costs against nursing hours. Use Excel or graph by hand.
3. Graph the hospital's overhead costs against the number of patient days. Use Excel or graph by hand.
4. Do the data appear to be sound, or do you see any potential data problems? Explain.
5. Use the high-low method to determine the hospital's cost equation using nursing hours as the cost driver. Predict total overhead costs if 25,500 nursing hours are predicted for the month.
6. Dawson runs a regression analysis using nursing hours as the cost driver to predict total hospital overhead costs. The Excel output from the regression analysis is as follows:

If 25,500 nursing hours are predicted for the month, what is the total predicted hospital overhead?
7. Dawson then ran the regression analysis using number of patient days as the cost driver. The Excel output from the regression is shown here:

If 3,680 patient days are predicted for the month, what is the total predicted hospital overhead?
8. Which regression analysis (using nursing hours or using number of patient days as the cost driver) produces the best cost equation? Explain your answer

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Number Hospital Overhead Overhead Cost per Cost per Nursing Hour Patient Day Overhead Nursing of Patient Costs Month Hours Days $483,000 24,500 28,500 July $19.71 $18.77 $21.08 $ 127.44 $ 124.13 $ 97.16 3,790 4,310 4,230 3,450 5,710 3,290 $535,000 $411,000 19,500 20,500 August September October November December $ 130.72 $ 100.88 $ 135.56 $451,000 $22.00 $576,000 $18.00 32,000 20,000 .......... $446,000 $22.30 SUMMARY OUTPUT – Nursing hours as cost driver Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA 0.984685 0.969605 0.962006 11,992.70481 df SS MS Significance F Regression Residual Total 1 18,352,033,459 18,352,033,459 127.599774 525,299,875 5 18,877,333,334 0.00035 4 143,824,969 Standard Error 25,618.85 1.041 Upper 95% 128,480.456 270,739.118 Lower Coefficients 199,609.79 t Stat P-value 95% Intercept X Variable 1 7.792 0.001 11.75 11.296 0.000 8.865 14.643