Assessment#3 Perform multiple regression on the relationship between hospital costs and patient age, risk factors, and patient satisfaction scores, and then generate a prediction to support this health care decision. Write a 3–4-page analysis of the results in a Word document and insert the test results into this document. Download the . The dataset contains the following variables: Hospital administration needs to make a decision on the amount of reimbursement required to cover expected costs for next year. For this assessment, using information on hospital discharges from last year, perform multiple regression on the relationship between hospital costs and patient age, risk factors, and patient satisfaction scores, and then generate a prediction to support this health care decision. Write a 3–4-page analysis of the results in a Word document and insert the test results into this document (copied from the output file and pasted into a Word document). Refer to for instructions. Submit both the Word document and the Excel file that shows the results. Your assessment should meet the following requirements:

Assessment#3: Perform Multiple Regression on the Relationship Between Hospital Costs and Patient Age, Risk Factors, and Patient Satisfaction Scores

Introduction:
In healthcare decision-making, it is essential to understand the factors that influence hospital costs. This assessment aims to perform multiple regression analysis to examine the relationship between hospital costs and variables such as patient age, risk factors, and patient satisfaction scores. The results of this analysis will assist hospital administration in making informed decisions on the amount of reimbursement required to cover expected costs for the upcoming year.

Methodology:
To conduct the multiple regression analysis, a dataset containing information on hospital discharges from the previous year will be utilized. The following variables will be considered:

1. Hospital costs: This variable represents the total costs incurred by the hospital for the treatment of patients. It serves as the dependent variable, as we aim to understand how it is affected by other independent variables.

2. Patient age: This variable represents the age of the patient at the time of admission. It is considered as an independent variable, as it may influence the hospital costs.

3. Risk factors: This variable represents the number of risk factors associated with the patient’s condition. It is also considered as an independent variable, as it may impact the hospital costs.

4. Patient satisfaction scores: This variable represents the patients’ satisfaction levels with the healthcare services provided by the hospital. It is included as an independent variable, as it may have an effect on the hospital costs.

The multiple regression analysis will involve fitting a linear regression equation to the data and estimating the coefficients for each independent variable. The assumptions of multiple regression, such as linearity, independence, normality, and homoscedasticity, will be assessed to ensure the validity of the analysis.

Results:
The results of the multiple regression analysis will provide insights into the relationship between hospital costs and the independent variables. It will allow us to understand how patient age, risk factors, and patient satisfaction scores impact hospital costs.

Specifically, the regression equation will quantify the impact of each independent variable on hospital costs. The coefficients associated with each variable will indicate the change in hospital costs that can be attributed to a unit change in the respective independent variable, holding all other variables constant. For example, if the coefficient for patient age is 0.1, it means that, on average, hospital costs increase by 0.1 units for each unit increase in patient age.

Additionally, the statistical significance of the coefficients will be assessed using hypothesis testing. The p-values associated with each coefficient will indicate whether the relationship between the independent variable and hospital costs is statistically significant. A low p-value (typically <0.05) suggests a significant relationship, while a high p-value (>0.05) suggests that the relationship may be due to chance.

Finally, a prediction model will be generated using the regression equation to support the healthcare decision. By inputting values for patient age, risk factors, and patient satisfaction scores into the equation, a predicted value for hospital costs can be obtained. This prediction will assist hospital administration in determining the amount of reimbursement required to cover expected costs for the next year.

Conclusion:
Performing a multiple regression analysis on the relationship between hospital costs and patient age, risk factors, and patient satisfaction scores provides valuable insights for healthcare decision-making. By understanding the impact of these variables on hospital costs, hospital administration can make informed decisions regarding reimbursement and budget planning. The results of the analysis, along with the prediction model, will aid in ensuring adequate financial resources for healthcare organizations while providing quality patient care.