Linear Regression Analysis Resource
Nurse participation and the nurse attrition rate are issues in the healthcare system. To reduce job-related stress among its employees, a hospital developed an employee well-being program to improve employee morale and reduce stress. The purpose of this study is to determine if there is a significant relationship between the monthly rate of nurse participation and the nurse attrition rate over 36 months. Categorized data for program participation and nurse attrition was extracted and assembled from the program enrollment and HR employee databases, with the data for analysis shown below. This essay will evaluate the efficacy of an employee well-being program in reducing job-related stress among the hospital’s nursing staff. A linear regression analysis will determine if there is a significant relationship between the monthly rate of nurse participation and the nurse attrition rate over 36 months. Categorized data for program participation and nurse attrition was extracted and assembled from the program enrollment and HR employee databases.
Question A
The primary inquiry that a hospital administration or executive is likely to pose is whether the Employee Well-Being Program confers financial benefits to the hospital. If the program fails to yield financial gains for the organization, it would be arduous to persuade the board of directors or shareholders to approve allocating funds toward sustaining the program. The inquiry of whether the Employee Wellness Program yields financial benefits for the hospital, as viewed by a data analyst, may be construed as an investigation into whether the expenses incurred by the program contribute to mitigating the expenses associated with worker turnover.
In the absence of precise cost particulars, it can be asserted that the expenses linked with attrition can be considered substantial. The expenses involved in the process of replacing an employee would encompass the expenses incurred in the off-boarding of the current employee, the expenses associated with advertising the open position, the expenses related to conducting interviews with potential candidates, which may include expenses for travel and lodging for candidates who reside outside the area, and ultimately, the expenses associated with providing training to the newly hired employee. The task of forecasting and allocating resources for the increasingly volatile cost of employee turnover can pose significant challenges.
Question B
The Linear Regression Analysis Resources data includes 36 months of data. The independent variable is the program participation rate, and the dependent variable is the nurse attrition rate. The data is categorical, as both variables are represented as percentages. This is a continuous ratio. There were 72 data points collected, but only 36 would be examined.
2. Create a graphical display of the data using a scatter plot or line chart, including each of the following:
The following line chart illustrates the relationship between the rate of program participation and nurse attrition rate over 36 months:
Chart Title: Rate of Program Participation vs. Nurse Attrition Rate
Legend: Rate of Program Participation (%), Nurse Attrition Rate (%)
Axis Titles: Program Participation Rate (%), Nurse Attrition Rate (%)
Data Intervals: y-axis = 5%
X-axis = un-uniform
Question C
1. Provide the output and calculations of the linear regression analysis you performed
To analyze the data, a linear regression analysis was performed. The linear regression output and calculations are shown in the table below:
Linear Regression Analysis Output and Calculations
| SUMMARY OUTPUT | |
| Regression Statistics | |
| Multiple R | 0.74428486 |
| R Square | 0.55395995 |
| Adjusted R Square | 0.54084112 |
| Standard Error | 0.82520629 |
| Observations | 36 |
| ANOVA | |||||
| df | SS | MS | F | Significance F | |
| Regression | 1 | 28.754676 | 28.75468 | 42.2263382 | 1.95603E-07 |
| Residual | 34 | 23.152824 | 0.680965 | ||
| Total | 35 | 51.9075 |
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 5.58955642 | 0.3746486 | 14.91946 | 1.7382E-16 | 4.828178767 | 6.3509341 | 4.8281788 | 6.350934083 |
| PPR% | -0.0849386 | 0.0130711 | -6.49818 | 1.956E-07 | -0.111502297 | -0.0583748 | -0.111502 | -0.05837483 |
2. Justify why linear regression is the appropriate analysis technique for predicting the dependent variable, including relevant details from the scenario to support your justification
Linear regression is an appropriate analysis technique for predicting the dependent variable, the nurse attrition rate, as it determines the strength of the linear relationship between two variables. The data indicate a relationship between nurse participation in the well-being program and the nurse attrition rate over 36 months. This linear regression analysis was used to measure the strength of the correlation, and the correlation coefficient of 0.20 reveals a weak relationship between the two variables, indicating that the well-being program may not significantly impact reducing the nurse attrition rate.
Question D
D. Describe the implications of your data analysis from the scenario by doing the following:
1. State the null hypothesis for this linear regression analysis….
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