Task 1: Linear Regression Analysis Scenario A major healthcare system wants to reduce job-related stress among its employees. In particular, the nursing staff endures a significant amount of pressure due to 12-hour rotating shifts, required overtime, and challenging job assignments. A few years ago, the hospital’s human resources (HR) department discovered that its nurse turnover rate was much higher than any other professional position at the hospital. Those nurses leaving employment cited work conditions and emotional stress as their main concerns. To curb this trend, the hospital proactively developed an employee well-being program. For over three years, a program manager has sponsored monthly activities specifically designed to improve employee morale and reduce stress. Participation in the program has been strictly voluntary, with a $50 cash bonus awarded to all employees who consistently complete the various well-being activities every quarter. Nurse participation is specifically being tracked to see if the program has successfully improved job satisfaction and reduced job turnover. Enrollment in the program has generally increased over time. Sponsored activities are currently developed by a full-time athletic trainer, a massage therapist, and a yoga instructor. These professionals also provide individualized services to participants upon request. A full-time program coordinator is tasked with administering the program’s day-to-day operations, including the responsibility of expanding employee participation through periodic marketing emails that tout program benefits and upcoming activities. Of course, there are significant internal costs associated with subsidizing this program. Nevertheless, it is widely believed that these expenses are minuscule when compared to the substantial costs associated with replacing employees—nurses in particular. Accordingly, the program costs and predicted savings are not being considered as part of this initial analysis. 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 the span of 36 months. Since attrition can be considered a lagging indicator of job satisfaction, the attrition rate is retroactively tied back to each nurse’s enrollment month. Categorized data for program participation and nurse attrition was extracted and assembled from the program enrollment database and the HR employee database, with the data for analysis shown below. The hospital executive council is reviewing the program’s efficacy in reducing nurse attrition through program participation as part of its routine funding plan for the next five years. However, the initial meeting will primarily focus on program efficacy and predictions for future enrollment growth and nurse retention.
A. Describe a business question that could be answered by applying linear regression analysis and is derived from the scenario in the attached “Linear Regression Analysis Resources.”
B. Describe the data provided in the attached “Linear Regression Analysis Resources” by doing the following:
1. Describe the relevant data characteristics for your linear regression analysis, including each of the following:
the independent variable(s)
the dependent variable
type of data
quantity of data
2. Graphical display of the data using a scatter plot or line chart, including each of the following:
chart title
legend
axis titles
data intervals
Note: This display should be a summary or representation of the data provided, not raw data.
C. Report how you analyzed the data using linear regression by doing the following:
1. Provide the output and calculations of the linear regression analysis you performed.
Note: You may submit the analysis output and calculations using a separate spreadsheet attachment or the optional template in the attached “Linear Regression Analysis Resources.”
Note: The output should include the output from the software you used to perform the analysis. Refer to “Prepare for the Performance Assessment Task 1” in the course of study to see examples of acceptable output.
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.
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.
2. Interpret the results of the data analysis by doing the following:
a. Discuss the goodness of fit with the supporting test statistic from your linear regression analysis output.
b. Discuss the significance of the independent variable(s) with support from your linear regression analysis results.
c. Create the linear equation and explain its purpose using your analysis results.
3. Discuss a limitation of the research that could affect a recommended course of action.
4. Recommend a course of action that aligns with your linear regression analysis results.
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