Interval-Estimation-of-Employee-Attrition

INterval estimation stats project

1. Interval Estimation of Employee Attrition A Statistical Approach using IBM HR Analytics Dataset Professor: Prof. R. K. Jana Team: RAO (C. R. Rao) – Group 1 Members: A. P. Advaith (25MBA001), Aashita Jumade (25MBA006), Atharva R (24PGP055)

2. Introduction: The Challenge of Employee Attrition Why Attrition Matters Critical HR concern affecting organisational performance Direct impact on recruitment costs and productivity Influences team morale and knowledge retention Limitations of Point Estimates Single values lack uncertainty quantification Insufficient for strategic planning decisions Benefits of Interval Estimation Provides realistic ranges for decision-making Quantifies uncertainty in estimates Enables robust workforce planning strategies

3. Research Agenda Calculate Attrition Rate Determine the proportion of employees leaving the organisation Apply Interval Estimation Construct confidence intervals using statistical methods Compare 95% & 99% CI Analyse different confidence levels for strategic insights Interpret Results Provide actionable recommendations for HR management Draw Conclusions Synthesise findings for workforce planning applications

4. Data Source: IBM HR Analytics Dataset Dataset Characteristics 1,470 Total Employees Comprehensive sample size ensuring statistical reliability 2 Attrition Categories Binary classification: Yes/No status for each employee Data Quality Features Real-world corporate dataset from IBM Meets large-sample statistical requirements Suitable for confidence interval construction

5. Methodology: Statistical Framework Confidence Interval Formula Sample Proportion \hat{p} represents the observed attrition rate in our dataset Critical Value z^* corresponds to the desired confidence level Standard Error Measures variability of the sample proportion estimate Sample Size n = 1,470 employees ensures large-sample validity

6. Methodology: Application to Dataset Critical Z-Values 1.96 95% Confidence Standard confidence level for business applications 2.576 99% Confidence Higher confidence with wider interval range Application Steps Calculate sample proportion from dataset Determine appropriate z-values Compute standard error Construct confidence intervals

7. Results: Attrition Rate Analysis Key Finding: 16.12% Attrition Rate Lower Bound % Upper Bound % 95% Confidence Interval [14.2%, 18.0%] Narrower range for routine planning 99% Confidence Interval [13.7%, 18.6%] Wider range for conservative estimates

8. Interpretation of Confidence Intervals Statistical Meaning 95% confidence: If we repeated sampling 100 times, 95 intervals would contain the true attrition rate 95% vs 99% Comparison Higher confidence requires wider intervals, trading precision for certainty HR Implications Plan for attrition between 14-18%, with recruitment and retention strategies aligned accordingly

9. Conclusion: Statistical Insights for HR Strategy Key Advantages Interval estimation superior to point estimates for strategic decision-making Realistic ranges enable robust workforce planning Uncertainty quantification supports risk management Statistical rigour enhances credibility of HR analytics Strategic Value Interval estimation transforms uncertain predictions into actionable insights , enabling proactive talent management and informed resource allocation decisions.

10. Key Recommendations Strategic Staffing Planning Plan staffing assuming attrition within 14–18% range rather than relying on single-point estimates Adopt Interval-Based Analytics Use interval estimates for strategic workforce planning to account for inherent uncertainty in projections Regular Monitoring System Track attrition trends continuously and re-compute confidence intervals as workforce composition changes Targeted Retention Policies Align retention initiatives (training, incentives, culture) to minimize attrition closer to lower CI bound