Louis Becker is the Chief Operating Officer at Union Hospital in Newark, New Jersey. 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. Becker 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. Becker then ran the regression analysis using number of patient days as the cost driver. The Excel output from the regression is as follows:

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.

Transcribed Image Text:

Number of Overhead Cost per Patient Days 3,720 4,320 4,220 3,470 5,690 3,210 Overhead Cost Hospital Nursing Nursing Hour $19.83 per Patient Day $127.96 $118.52 $100.47 Month Overhead Costs Hours 24,000 26,000 20,000 22,500 $476,000 July $512,000 $19.69 $21.20 $19.91 August September $424,000 ...*******.. October November December $448,000 $129.11 $ 97.54 $555,000 $18.50 $19.59 30,000 22,000 $431,000 $134.27 SUMMARY OUTPUT- Nursing hours as cost driver Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA 0.984895 0.970018 0.962523 9,883.836352 6. MS Significance F 0.000341 df SS Regression Residual Total 1 12,642,572,449 12,642,572,449 129.414926 390,760,884 5 13,033,333,333 4 97,690,221 Standard Error Upper 95% 46,466.228 215,543.149 Lower t Stat Coefficients 131,004.69 P-value 95% Intercept X Variable 1 30,448.454 1.253 4.303 0.013 14.26 11.376 0.000 10.777 17.735