I’m working on a Statistics question and need guidance to help me study.
University of Maryland University College
Dataset: HMGTFINALEXAM.csv (To be provided when you complete Assignment #3)
Question #1 (15 credits):
The FINAL EXAM dataset provides some information about hospitals in 2011 and 2012. Analyze the FINAL EXAM dataset. You may calculate the “Hospital Beds per Population (Per Capita)” variable by dividing “total_hospital_beds by tot_population. Use the analysis results to complete Table 1 below.
Table 1. Descriptive statistics about hospitals in 2011 & 2012
2011 
2012 
t Value (Pr<t) 

N 
Mean 
St. Dev 
N 
Mean 
St. Dev 

Hospital Characteristics 

1. Hospital beds 

2. Number of paid Employee 

3. Number of nonpaid Employee 

4. Internes and Residents 

5. System Membership 

6. Total hospital cost 

7. Total hospital revenues 

8. Hospital net benefit 

9. Available Medicare days 

10. Available Medicaid days 

11. Total Hospital Discharge 

12. Medicare discharge 

13. Medicaid discharge 

SocioEconomic Variables 

14. Hospital Beds per Population (Per Capita) 

15. Percent of population in poverty 

16. Percent of Female population in poverty 

17.Percent of Male population in poverty 

18. Median Household Income 
Then please answer the following questions.
Question #2 (15 credits):
In the dataset, create a new variable called hospital net benefits. Do this by subtracting hospital costs from hospital revenues.
Analyze the dataset and then complete Table 2. In the last column report the Ttest results, to compare hospital characteristics and the nationwide socioeconomic variables for forprofit and nonprofit hospitals.
Table 2. Descriptive statistics between teaching and nonteaching hospitals, 2011 & 2012
For Profit 
NonForProfit 
pvalue 

N 
Mean 
St. Dev 
N 
Mean 
St. Dev 

Hospital Characteristics 

1. Hospital beds 

2. Number of paid Employee 

3. Number of nonpaid Employee 

4. Internes and Residents 

5. System Membership 

6. Total hospital cost 

7. Total hospital revenues 

8. Hospital net benefit 

9. Available Medicare days 

10. Available Medicaid days 

11. Total Hospital Discharge 

12. Medicare discharge 

13. Medicaid discharge 

SocioEconomic Variables 

14. Hospital Beds per Population (Per Capita) 

15. Percent of population in poverty 

16. Percent of Female population in poverty 

17. Percent of Male population in poverty 

18. Median Household Income 
Then answer the following questions:
Question #3 (15 credits):
The dataset provides the variable herf_ins called the Herfindahl–Hirschman Index which measures market concentration for the health insurance market. Please note that unlike the class exercise in which you used herf_cat, which measured market concentration for the hospital market, in this assignment you are using herf_ins which measures market concentration for the health insurance market.
Analyze the data to complete Table 3 (below):
Table 3. Comparing hospital characteristics and market, 2011 and 2012
High Competitive Market 
Moderate Competitive Market 
Low Competitive Market 
ANOVA/ChiSq (results) 

N 
Mean 
STD 
N 
Mean 
STD 
N 
Mean 
STD 

Hospital Characteristics 

1. Hospital beds 

2. Number of paid Employee 

3. Number of nonpaid Employee 

4. Internes and Residents 

5. System Membership 

6. Total hospital cost 

7. Total hospital revenues 

8. Hospital net benefit 

9. Available Medicare days 

10. Available Medicaid days 

11. Total Hospital Discharge 

12. Medicare dischargeratio 

13. Medicaid dischargeratio 

SocioEconomic Variables 

14. Hospital Beds per Population (Per Capita) 

15. Median Household Income 
Then answer the following questions:
(Note: to answer the last question, please compute Medicaredischarge ratios and Medicaiddischarge ratios first and then run two tTests (high competitive vs. moderate competitive, and high vs. low competitive market). Please support your findings with a boxplot).
Question #4 (Credits 20) Excel version
If you have chosen to work with Excel, please run the models and complete the following tables.
Regression Model 1:
Analyze the data by running a linear regression model as depicted in Table 4 below.
Table 4 – Regression Model 1
Coefficient 
ST. ERR 
T Stat 
Pvalues 
Lower 95% 
Upper 95% 

Intercept/Constant 

Total Hospital beds 

Teaching Hospital Dummy 

N = 

R Square = 
Regression Model 2:
Analyze the data by running a linear regression model as depicted in Table 5 below.
Table 5 – Regression Model 2
Coefficient 
ST. ERR 
T Stat 
Pvalues 
Lower 95% 
Upper 95% 

Intercept/Constant 

Total Hospital beds 

Teaching Hospital Dummy 

N = 

R Square = 
Regression Model 3:
Analyze the data by running a linear regression model as depicted in Table 5 below.
Table 6 – Regression Model 3
Coefficient 
ST. ERR 
T Stat 
Pvalues 
Lower 95% 
Upper 95% 

Intercept/Constant 

Total Hospital beds 

Teaching Hosp. Dummy 

Medicare discharge ratio 

Medicaid discharge ratio 

N = 

R Square = 
Regression Model 4:
Analyze the data by running a linear regression model as depicted in Table 7 below.
Table 7 – Regression Model 4
Coefficient 
ST. ERR 
T Stat 
Pvalues 
Lower 95% 
Upper 95% 

Intercept/Constant 

Total Hospital beds 

NonTeaching Hosp. Dummy 

Medicare discharge ratio 

Medicaid discharge ratio 

N = 

R Square = 
Question #5 (Credits 20) Excel version
If you have chosen to work with Excel, please run three models and complete the following tables.
Logistic Regression Models
If you have chosen to work with RStudio, please run the following model and complete the following tables.
Logistic Model 1:
Analyze the data by running a logistic regression model as depicted in Table 8 below. Use “being a member of a hospital network” (system_member) as the dependent variable.
Table 8 – Logistic Model 1
Coefficient 
ST. ERR 
PValue 
Exp (coeff) 
Exp (z SE) 
Exp (Std. Coeff.) 

Intercept/Constant 

Total Hospital costs 

N = 

R Square = 
Logistic Model 2:
Analyze the data by running a logistic regression model as depicted in Table 9 below.
Table 9 – Logistic Model 2
Coefficient 
ST. ERR 
PValue 
Exp (coeff) 
Exp (z SE) 
Exp (Std. Coeff.) 

Intercept/Constant 

Total Hospital Costs 

Total Hospital Revenue 

N = 

R Square = 
Logistic Model 3:
Analyze the data by running a logistic regression model as depicted in Table 10 below.
Table 10 – Logistic Regression Model 3
Coefficient 
ST. ERR 
PValue 
Exp (coeff) 
Exp (z SE) 
Exp (Std. Coeff.) 

Intercept/Constant 

Total Hospital Costs 

Total Hospital Revenue 

Medicare discharge ratio 

Medicaid discharge ratio 

N = 

R Square = 
Question 6 (15 credits)
1. Please offer a research question for the study using human subject research.
2. Explain the difference between the research process involving human subjects and the research process not involving human subjects.
3. Discuss ethical implications surrounding human subject research studies.
4. Explain the governance of the human subject research studies over the data and the process.
5. Provide examples of the consequences for not meeting IRB (Institutional Review Board) protocol requirements.