Statistics
If you have chosen to work with Excel, run the three linear regression models and complete the following tables using the dataset from week 1’s exercise.
Medicare and Medicaid Discharge Ratios: Medicare Discharges ÷ Total Hospital Discharges; Medicaid Discharges ÷ Total Hospital Discharges
Model 1:
Run a linear model to predict the impact of number of hospital beds (use bed-tot) on hospital net-benefit in teaching hospitals.
Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
R Square
(Limit all results to 2 decimal places max)
Model 2:
Run a linear model to predict the impact of number of hospital beds (use bed-tot) on hospital net-benefit in non-teaching hospitals.
Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
R Square
(Limit all results to 2 decimal places max)
Use the results from model 1 and model 2 and compare the results between teaching and non-teaching hospitals.
Model 3:
Now, include the Medicare and Medicaid discharge ratios in first model. How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital net-benefit in teaching hospitals?
Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
Medicare-discharge-ratio
Medicaid-discharge-ratio
R Square
(Limit all results to 2 decimal places max)
Model 4:
Now, include the Medicare and Medicaid discharge ratios in first model. How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital net-benefit in non-teaching hospitals?
Hospital Characteristics Coef. ST. ERR T Stat P-values Lower 95% Upper 95%
Hospital beds
Medicare-discharge-ratio
Medicaid-discharge-ratio
R Square
(Limit all results to 2 decimal places max)
Based on your findings, recommend 3 policies to improve hospital performance. Make sure to use the final model for your recommendations.
