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C207 Task 1 Data-Driven Decision Making, Western’s Governor’s University.

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C207 Task 1 Data-Driven Decision Making, Western’s Governor’s University.










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June 11, 2025
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C207 Task 1 Data - Driven Decision Making , Western’s Governor’s University .




A. Business Scenario Summary

A1. Business Question

The hospital is implementing a wellness initiative to reduce nurse turnover in this

scenario. It recognizes the demanding nature of their roles and aims to improve retention and

support staff well-being. The business question is: Does providing incentives for a wellness

program effectively reduce nurse turnover at this hospital?

A2. Null Hypothesis

The null hypothesis (H₀) suggests that no significant relationship exists between the

monthly rate of nurse participation in well-being programs and the nurse attrition rate. This

implies that variations in nurse turnover are independent of their engagement in such

programs. A 2024 scoping review by Almeida et al examined nurses' well-being in hospitals,

analyzing eight studies on workplace satisfaction and managerial strategies. Findings showed

that most nurses were content with their jobs, with key contributing factors including positive

management interactions, a supportive workplace culture, and strong organizational

commitment. The review emphasized the crucial role of nurse managers in creating a positive

work environment, ensuring nurses feel valued and motivated. Leadership also contributes

value by enhancing job performance and patient care quality.

A3. Justification for Linear Regression

Linear regression is an appropriate analysis technique for this task because it evaluates

the strength and direction of the relationship between two continuous variables: program

participation rate (independent variable) and nurse attrition rate (dependent variable). The goal

is to determine whether participation rate can predict attrition trends over time.

, C207 Task 1 Data - Driven Decision Making , Western’s Governor’s University .




B. Data Description

B1. Relevant Data Characteristics
 Independent Variable: Program Participation Rate (%)
 Dependent Variable: Nurse Attrition Rate (%)
 Level of Measurement:
o Program Participation Rate: Ratio (percentage values)
o Nurse Attrition Rate: Ratio (percentage values)
 Sample Size: 72 data points (2 data points per month for 36 months)

C. Data Analysis Using Linear Regression

C1. Regression Output and Calculations
Using Ordinary Least Squares (OLS) regression, the following results were obtained:
Regression Equation:
Nurse Attrition Rate = 4.5033 - 0.0781 × Program Participation Rate
 R-squared: 0.563 (56.3% of the variance in nurse attrition rate is explained by
participation rate)
 Adjusted R-squared: 0.550
 P-value for participation rate: 0.0000139% (highly significant)
(1.39E - 07 × 100 = 0.0000139%)
 F-statistic: 43.73 (suggesting strong predictive power)
Standard Error and Confidence Interval
 Standard Error of the Coefficient: 0.0118
 95% Confidence Interval for the Coefficient (-0.0781): (−0.1021, −0.0541)
Interpretation
The standard error of 0.0118 suggests a relatively low variability in the estimate. The
95% confidence interval indicates that the true effect of program participation on
nurse attrition is likely between -0.1021 and -0.0541. Since the confidence interval
does not include 0, this reinforces that the negative relationship is statistically
significant.

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