Report on Calculation of Administrative Cost

Added on - 21 Apr 2020

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Question 11.2004006008001000120014001600$-$2,000.00$4,000.00$6,000.00$8,000.00$10,000.00$12,000.00$14,000.00$16,000.00$18,000.00AdministrativeCosts2.High-low method1500 3001016100 1500 10116100 4100100VariablecostFixedcostEquation1100 10TotaladministrativecostsPatientLoad3.Calculation of Administrative cost
1100 108001100 10 800091001100TotaladministrativecostsPatientLoad4.Regression analysisa)Equation with patient load predicting administrative costSUMMARY OUTPUTRegression StatisticsMultiple R0.998827319R Square0.997656012Adjusted R Square0.997442923Standard Error1515.228131Observations13ANOVAdfSSMSFSignificance FRegression1107491526131.07E+104681.868.0644E-16Residual1125255079.162295916Total1210774407692CoefficientsStandardErrort StatP-valueLower95%Upper95%Lower95.0%Upper95.0%Intercept94.932543495.29764840.1916680.85149-995.210231185.075-995.210231185.08X10.6956820.15631455268.42418.1E-1610.351636411.0397310.351636411.039794.9325 10.69TotaladministrativecostsPatientLoadb.Calculation of administrative cost
94.93 10.6994.93 10.69 80094.93 8556.58651.448TotaladministrativecostsPatientLoadc.Equation with patient load and number of emergency procedures predictingadministrative costSUMMARY OUTPUTRegression StatisticsMultiple R0.963963269R Square0.929225184Adjusted R Square0.913497448Standard Error972.7451213Observations12ANOVAdfSSMSFSignificance FRegression21118105695590528559.08196.675E-06Residual98516097.639946233.1Total11120326666.7CoefficientsStandardErrort StatP-valueLower95%Upper95%Lower95.0%Upper95.0%Intercept1769.33111029.3276751.7189190.11975-559.169844097.832-559.169844097.83Numberofemergencyprocedures148.50741122.09062141.216370.25478-127.680764424.6956-127.68076424.696
Patientload7.10133950.929748017.6379183.2E-054.998103379.2045764.998103379.204581769.331148.50747.101minc9o33sNumberofemerTotaladistragencyproceduresPatietntivloatsded.Calculation of administrative cost1769.331148.50747.1013391769.331 12 148.5074 800 7.1013391769.331 1782.0889 5681.07169232.4916NumberofemergencyproceduresPatientloTotaladministrativecosatsd5.Yes, the inclusion of additional variable improves the results when standard error becomeshigher due to inclusion. In the present case, the standard error for the patient load becomeshigher due to the inclusion of the number of emergency procedures as one more independentvariable hence it can be concluded that inclusion of additional cost driver improves themodel.6.The high low method only considers the ingest and lowest value of cost driver and datavalues related to that Cost driver on the other hand regression analysis considers all datavalues[ CITATION Bam08 \l 1033 ]. Therefore in makinga comparison between regression andhigh low method it can be concluded that regression model can provide more significantresults. Moreover, in the part %, it was concluded that multiple regression improves themodel.Hence McDonough should be highest confident towards multiple regression analysis andlowest confident about the high low model.
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