Running Head: Final Project Part 2 Week 7 Shaun Billingslea February 18, 2012 Walden University- Research and Program Evaluation (HINF XXXXXXXXXXINTRODUCTION Statistics has come to play an important...

Running Head:
Final Project Part 2



Week 7


Shaun Billingslea


February 18, 2012

Walden University- Research and Program Evaluation (HINF - 6205 - 11)

INTRODUCTION

Statistics has come to play an important role in almost every field of life and human activity. There is hardly any field where statistical data or statistical methods are used for one purpose or the other our arrival in this world and departure from here are recorded as statistical data somewhere and in same form.
We have to find the relationship between Total operating expense_05 as a dependent variable with respect to other independent variables. We need to analysis what is the effect of changing one unit in one variable and how the dependent variable behaves with the change. We need to find the significant variables who are actually affecting the dependent variable. We need to find the regression equation with the help of regression analysis so that we can make better predictions using the data and available information.

DATA

Here in this report we have a data for 81 subjects with 0 missing values. The dependent and independent variables are as follow:
Total operating expense_05 (Dependent variable)
Staffed beds_05
Medicare Days_05
Medicaid Days_05
Total Surgeries_05
RN FTE_05
Patient days_05/(Licensed beds_05 x 365)
Ownership
System Membership
Rural/Urban
Teaching Affiliation
Age 65 Plus 2005
Crime rate/100,000 population (2005)
Uninsured 2005
These all variables are scale variables except Ownership, System Membership, Rural/Urban and Teaching Affiliation

METHODLOGY

Here we can use many statistical methods to analyses this data. The main methods which can be used in this analysis are known as Descriptive methods, dispersion, correctional analysis, multiple regression analysis,

ANALYSIS

We have done the analysis using the above specified methods. For all the independent variables the descriptive statistics is obtained and multiple regression is obtained using all the variables as independent variables such as : Staffed beds_05 Medicare Days_05 Medicaid Days_05 Total Surgeries_05 RN FTE_05 Occupancy Ownership System Membership Rural/Urban Teaching Affiliation Age 65+ Crime Rate Uninsured and the dependent variable is Total operating expense_05. All the analysis is performed using the MS Excel software.

RESULT, OUTPUT and CONCLUSION

Here we are just pasting the regression output, for descriptive statistics please refer to the excel 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

From the above regression analysis we can see from ANOVA table that the model is statistically significant at 5% level of significance. Also the independent variables are significant at 5% level of significance because the p-values are .0000 for 4 independent variables. The Regression equation is given by

Total operating expense_05 = 351318.19 + 171601.98*Staffed beds_05 -1172.87*Medicare Days_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*Teaching Affiliation -502.59*Age 65 Plus 2005 -64.28*Crime rate/100,000 population (2005) +544.51*Uninsured 2005

The coefficient of determinant is very good in this model. R-square tells us that about the variable in the dependent variable explained by the independent variables. Here 96.90% of the variation is explained by the independent variables.
Here we have only four independent variables which are significant at 5% level of significance and those variables are Staffed beds_05, Medicare Days_05, Total Surgeries_05 and RN FTE_05. The positive sign of variable tells us that if there is one unit increase in the independent variable and other things remain constants then the dependent variable will increase by that quantity and also if the sign of variable is negative which tells us that if there is one unit increase in the independent variable and other things remain constants then the dependent variable will decrease by that quantity. For example if there is an increment in the one unit of Staffed beds_05 and other thing does not change then there will be an increment of 171601.98 in Total operating expense_05 and same explanation is true for all other variables.