DATA-DRIVEN DECISION MAKING C207
C207 Task 1: Linear Regression Analysis on Nurse Attrition Rates
Data-Driven Decision Making C207
Task 1 Linear Regression Analysis
2026
, DATA-DRIVEN DECISION MAKING C207
Predicting Nurse Attrition Through Well-being Program Participation: A Linear
Regression Analysis
Abstract
High nurse attrition rates pose significant operational and financial challenges in modern
healthcare settings. This study investigates the impact of employee well-being program
participation on nurse turnover using a linear regression framework over a 36-month period. By
analyzing 72 specific data points, we establish a statistically significant inverse relationship
where increased program engagement predicts lower attrition rates, ultimately providing
actionable data-driven strategies for healthcare administration and workforce retention.
Introduction
Healthcare organizations increasingly rely on data-driven decision-making to optimize
workforce management and improve operational efficiency. A critical metric in this domain is
the nurse attrition rate, which directly impacts patient care quality and organizational costs. The
central problem of this research centers on determining whether voluntary employee well-being
programs serve as an effective intervention to mitigate this high turnover. By investigating
nurses' monthly participation in these programs, organizational leaders hope to combat staffing
shortages and create more sustainable work environments.
Existing approaches to evaluating attrition are often insufficient for several primary reasons.
First, human resources departments frequently rely on anecdotal evidence or basic descriptive
statistics rather than robust predictive modeling. Second, traditional evaluations often ignore the
temporal dynamics of program participation, failing to capture how longitudinal engagement
C207 Task 1: Linear Regression Analysis on Nurse Attrition Rates
Data-Driven Decision Making C207
Task 1 Linear Regression Analysis
2026
, DATA-DRIVEN DECISION MAKING C207
Predicting Nurse Attrition Through Well-being Program Participation: A Linear
Regression Analysis
Abstract
High nurse attrition rates pose significant operational and financial challenges in modern
healthcare settings. This study investigates the impact of employee well-being program
participation on nurse turnover using a linear regression framework over a 36-month period. By
analyzing 72 specific data points, we establish a statistically significant inverse relationship
where increased program engagement predicts lower attrition rates, ultimately providing
actionable data-driven strategies for healthcare administration and workforce retention.
Introduction
Healthcare organizations increasingly rely on data-driven decision-making to optimize
workforce management and improve operational efficiency. A critical metric in this domain is
the nurse attrition rate, which directly impacts patient care quality and organizational costs. The
central problem of this research centers on determining whether voluntary employee well-being
programs serve as an effective intervention to mitigate this high turnover. By investigating
nurses' monthly participation in these programs, organizational leaders hope to combat staffing
shortages and create more sustainable work environments.
Existing approaches to evaluating attrition are often insufficient for several primary reasons.
First, human resources departments frequently rely on anecdotal evidence or basic descriptive
statistics rather than robust predictive modeling. Second, traditional evaluations often ignore the
temporal dynamics of program participation, failing to capture how longitudinal engagement
