1. **Stating the problem:** The analysis investigates determinants of effective Monitoring and Evaluation (M&E) systems in NGOs in Nairobi County using multiple linear regression. 2. **Regression model:** The model is given by: $$ EME = \beta_0 + \beta_1 STT + \beta_2 RMGT + \beta_3 MET + \beta_4 TES + \varepsilon $$ where $EME$ is the effective M&E system, $STT$ is selection of tools and techniques, $RMGT$ is role of management, $MET$ is M&E training, $TES$ is technical expertise, $\beta_i$ are parameters, and $\varepsilon$ is the error term. 3. **Estimated regression equation:** Using the coefficients from the data: $$ EME = 11.132 + 0.231 STT + 0.321 RMGT + 0.553 MET + 0.734 TES + \varepsilon $$ 4. **Interpretation of coefficients:** Each $\beta$ represents the expected change in $EME$ for a one-unit increase in the predictor, holding others constant. - $0.231$ for $STT$ means a small positive effect. - $0.321$ for $RMGT$ indicates a moderate positive effect. - $0.553$ for $MET$ shows a stronger positive effect. - $0.734$ for $TES$ shows the strongest positive effect. 5. **Statistical significance:** At 5% significance level: - $STT$ has $p=0.081$ (not significant). - $RMGT$ has $p=0.022$ (significant). - $MET$ has $p=0.053$ (marginally significant). - $TES$ has $p=0.013$ (significant). 6. **Model fit:** The coefficient of determination $R^2 = 0.785$ and adjusted $R^2 = 0.776$ indicate that about 77.6% of the variation in $EME$ is explained by the predictors. 7. **F-test:** The F statistic of $18.33$ with $p < 0.05$ shows the overall model is statistically significant. 8. **Correlation analysis:** Pearson correlation coefficients show positive relationships between $EME$ and all predictors, strongest with $TES$ ($0.713$), followed by $MET$ ($0.614$), $RMGT$ ($0.524$), and $STT$ ($0.511$). 9. **Conclusion:** The analysis identifies role of management, M&E training, and technical expertise as significant determinants of effective M&E systems in NGOs in Nairobi County, with technical expertise having the strongest influence. Selection of tools and techniques has a positive but not statistically significant effect at 5% level. The model explains a large portion of variability in effectiveness, supporting the importance of these factors in improving M&E systems.