avewse rystbseyetyb

1. Multiple Regression Analysis of Performance
Ratings
A Statistical Analysis of Factors Influencing Employee
Performance

2. Presentation Outline
• 1. Introduction and Research Objectives
• 2. Literature Review and Theoretical Framework
• 3. Methodology and Data Collection
• 4. Exploratory Data Analysis
• 5. Multiple Regression Analysis
• 6. Results and Interpretation
• 7. Discussion and Implications
• 8. Limitations and Future Research
• 9. Conclusion

3. 1. Introduction and Research Objectives

4. Research Objectives
• • To identify key factors influencing employee performance ratings
• • To quantify the relationship between predictors and performance
• • To develop a predictive model for performance evaluation
• • To provide evidence-based recommendations for performance improvement

5. 2. Literature Review and Theoretical Framework

6. Theoretical Framework
• • Performance Management Theory (Armstrong & Baron, 2005)
• • Human Capital Theory (Becker, 1964)
• • Goal-Setting Theory (Locke & Latham, 1990)
• • Expectancy Theory (Vroom, 1964)
• • Previous empirical studies have identified training, skills, attendance, and
feedback as key performance predictors

7. 3. Methodology and Data Collection

8. Research Methodology
• • Research Design: Quantitative, cross-sectional study
• • Data Collection: Survey questionnaire administered to employees
• • Sample: Employees from various departments and job levels
• • Variables Measured:
• - Dependent Variable: Performance Rating (1-10 scale)
• - Independent Variables: Training programs, absenteeism, salary, technical skills,
project completion rate, feedback frequency

9. 4. Exploratory Data Analysis

10. Correlation Analysis
Figure 1: Correlation matrix showing relationships between all variables in the study

11. Bivariate Relationships
Figure 2: Scatter plots showing relationships between each predictor and performance ratings

12. 5. Multiple Regression Analysis

13. Multiple Regression Model
• • Model Specification: Performance = β₀ + β₁(Training) + β₂(Absenteeism) +
β₃(Salary) + β₄(Skills) + β₅(Project Completion) + β₆(Feedback) + ε
• • Estimation Method: Ordinary Least Squares (OLS)
• • Diagnostic Tests: Multicollinearity (VIF), Heteroscedasticity, Normality of
residuals
• • Software Used: Python with statsmodels, pandas, and matplotlib libraries

14. Multicollinearity Diagnostics
Figure 3: Variance Inflation Factors (VIF) for each predictor variable

15. 6. Results and Interpretation

16. Statistical Significance of Predictors
Figure 4: P-values for each predictor variable (values below 0.05 are statistically significant)

17. Model Explanatory Power
Figure 5: R-squared decomposition showing contribution of each variable to explained variance

18. Key Findings
• • The overall model explains 36.3% of variance in performance ratings (R² = 0.363)
• • Project Completion Rate is the only statistically significant predictor (p < 0.05)
• • A 1% increase in Project Completion Rate is associated with a 0.08 increase in
performance rating
• • Other predictors did not reach statistical significance at the 0.05 level
• • No significant multicollinearity issues detected (all VIF values < 5)

19. 7. Discussion and Implications

20. Discussion and Implications
al Implications:
support Goal-Setting Theory
completion as a key performance indicator
ges traditional views on training and salary as primary motivators
s a task-oriented approach to performance
Practical Implications:
• Focus on project management skills
• Implement project tracking systems
• Set clear project milestones and deadlines
• Recognize and reward project completion
• Reconsider traditional performance metrics

21. 8. Limitations and Future Research

22. Limitations and Future Research
• Limitations:
• • Cross-sectional design limits causal inferences
• • Self-reported data may introduce bias
• • Sample size and representativeness
• • Unmeasured variables may influence performance
• Future Research Directions:
• • Longitudinal studies to establish causality
• • Include additional predictors (e.g., leadership style, organizational culture)
• • Explore interaction effects between predictors
• • Industry-specific and cross-cultural comparisons

23. 9. Conclusion

24. Conclusion
• • Project completion rate emerges as the most significant predictor of
performance ratings
• • The findings challenge conventional wisdom about performance drivers
• • Organizations should prioritize project management capabilities and completion
metrics
• • Performance management systems should be redesigned to emphasize task
completion
• • Further research is needed to validate these findings across different contexts
• This study contributes to both theory and practice by identifying key drivers of
employee performance in the modern workplace.

25. References
strong, M., & Baron, A. (2005). Managing performance: Performance management in action. CIPD Publishing.
er, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago P
e, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Prentice-Hall.
m, V. H. (1964). Work and motivation. Wiley.
J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

26. Thank You
Questions & Discussion