Running Head: Final Project Part 2 Week 7 Shaun Billingslea February 18, 2012 Walden University-…

Running Head: Final Project Part 2 Week 7 Shaun Billingslea February 18, 2012 Walden University-…

RunningHead: Final Project Part 2Week 7Shaun BillingsleaFebruary 18, 2012Walden University- Research and Program Evaluation (HINF –6205 – 11)
INTRODUCTIONStatistics has come toplay an important role in almost every field of life and human activity. Thereis hardly any field where statistical data or statistical methods are used forone purpose or the other our arrival in this world and departure from here arerecorded as statistical data somewhere and in same form.We have to find therelationship between Total operating expense_05 as a dependent variable withrespect to other independent variables. We need to analysis what is the effectof changing one unit in one variable and how the dependent variable behaveswith the change. We need to find the significant variables who are actuallyaffecting the dependent variable. We need to find the regression equation withthe help of regression analysis so that we can make better predictions usingthe data and available information.DATAHere in this report wehave a data for 81 subjects with 0 missing values. The dependent andindependent variables are as follow:Total operatingexpense_05 (Dependent variable)Staffed beds_05Medicare Days_05Medicaid Days_05Total Surgeries_05RN FTE_05Patientdays_05/(Licensed beds_05 x 365)OwnershipSystem MembershipRural/UrbanTeaching AffiliationAge 65 Plus 2005Crime rate/100,000population (2005)Uninsured 2005These all variables arescale variables except Ownership, System Membership, Rural/Urban and TeachingAffiliationMETHODLOGYHere we can use manystatistical methods to analyses this data. The main methods which can be usedin this analysis are known as Descriptive methods, dispersion, correctionalanalysis, multiple regression analysis,ANALYSISWe have done theanalysis using the above specified methods. For all the independent variablesthe descriptive statistics is obtained and multiple regression is obtainedusing all the variables as independent variables such as : Staffed beds_05Medicare Days_05 Medicaid Days_05 Total Surgeries_05 RN FTE_05 OccupancyOwnership System Membership Rural/Urban Teaching Affiliation Age 65+ Crime RateUninsured and the dependent variable is Totaloperating expense_05. All the analysisis performed using the MS Excel software.RESULT,OUTPUT and CONCLUSIONHere we are justpasting the regression output, for descriptive statistics please refer to theexcel file as that output is large and will not be pasted here.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.984382662
R Square
0.969009225
Adjusted R Square
0.962996089
Standard Error
28553033.89
Observations
81
ANOVA
df
SS
MS
F
Significance F
Regression
13
1.70795E+18
1.31381E+17
161.1487414
3.39504E-45
Residual
67
5.46235E+16
8.15276E+14
Total
80
1.76257E+18
.gif”>From the aboveregression analysis we can see from ANOVA table that the model is statisticallysignificant at 5% level of significance. Also the independent variables are significantat 5% level of significance because the p-values are .0000 for 4 independent variables.The Regression equation is given byTotaloperating expense_05 = 351318.19 + 171601.98*Staffed beds_05 -1172.87*MedicareDays_05 – 432.35*Medicaid Days_05 1931.09*Total Surgeries_05 +341925*RN FTE_05+32576.35*Patient days_05/(Licensed beds_05 x 365) -15524429.66*Ownership+6947151.09*System Membership -76288866.38*Rural/Urban -16811968.14*TeachingAffiliation -502.59*Age 65 Plus 2005 -64.28*Crime rate/100,000 population(2005) +544.51*Uninsured 2005The coefficient ofdeterminant is very good in this model. R-square tells us that about thevariable in the dependent variable explained by the independent variables. Here96.90% of the variation is explained by the independent variables.Here we have only fourindependent variables which are significant at 5% level of significance andthose variables are Staffed beds_05, Medicare Days_05, Total Surgeries_05 and RNFTE_05. The positive sign of variable tells us that if there is one unit increasein the independent variable and other things remain constants then thedependent variable will increase by that quantity and also if the sign ofvariable is negative which tells us that if there is one unit increase in theindependent variable and other things remain constants then the dependentvariable will decrease by that quantity. For example if there is an incrementin the one unit of Staffed beds_05 and other thing does not change then therewill be an increment of 171601.98 in Total operating expense_05 and sameexplanation is true for all other variables.