Question:
Freida Dudley is the Chief Operating Officer at Memorial Hospital in Scandia, Minnesota. 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 23,500 nursing hours are predicted for the month.
6. Ms. Dudley 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 shown next:
If 23,500 nursing hours are predicted for the month, what is the total predicted hospital overhead?
7. Ms. Dudley then ran the regression analysis using number of patient days as the cost driver. The Excel output from the regression is shown here:
If 3,300 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:
Hospital Number Cost per Overhead Nursing of Patient Nursing Month July August September Days $462,000 22,900 3,610 Costs Hours Hour Patient Day S20.17 $127.98 S510,000 26,300 4,330 19.39 $117.78 S401,000 7,500 4,250 22.91 $445,000 21,700 3,460 20.51 $ 94.35 $128.61 November S556,000 30,000 5,740 $18.53 $ 96.86 $430,000 19,000 3,230 22.63 $133.13 December 1 SUMMARY OUTPUT Regression Statistics 4Multiple R 5 R Square 6 Adjusted R Square 0.9938 0.9876 0.9845 7027.6715 7 Standard Eror 8 Observations 10 ANOVA 12 Regression 13 Residual 14 Total 15 1580578 1580578 320.031 3.0254 19755 49388167.25 1600333 P-value Coefficients Standard Error 15764.911 0.677 t Stat 95% 95% 95.0% 95.0% 17 Intercept190017.690 18 X Variable 1 146247.280 233788.101 146247.280 233788.101 12.053 17.889 0.000 0.000 12.110 10.230 13.989 10.230 13.989 1 SUMMARY OUTPUT StatisticS 4Multiple R 0.75340 0.5676 0.45953 41591.550 s Adjusted R Square 7 Standard Error 8 Observations 10 ANOVA MS Significance F 0.08371 5.25124 13 Residual 14 Total 15 691942 172985 1600333 16 17 Intercept275852.530 18 X Variable 1 Coefficients Standard Error 85266.887 20.364 t Stat P-value 95% 95% 95.0% 95.0% 3.235 0.032 39113.700 512591.364 39113.700512591.364 103.203 46.660 0.084 -9.874 103.203 -9.874