# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Regression Model and Applicability
The graphical analyses in Figures 5 and 6 indicate that the model follows a normal distribution and that the residuals are randomly distributed. The Pareto chart has identified AT, M\(\times\)AT, M, and A as the most influential factors. Contour plots show that the optimal response variable is obtained in the red and yellow regions, with AT and M factors being the  
Figure 4: Contour plots of T, illustrating the interaction effects of variables on the response, with specific hold values for parameters A, M, WS, and AT for Leaf Plants. key determinants. In conclusion, the AT factor is the strongest variable, and its interaction with M enhances the predictive power of the model. \begin{table} \begin{tabular}{l c c c c c c c} \hline \hline
**Source** & **DF** & **Contribution** & **Adj SS** & **Adj MS** & **F-Value** & **\(p\)-Value** & **VIF** \\ \hline Model & 14 & 91.40\% & 7720.51 & 551.465 & 70.58 & 0.000 & \\ Linear & 4 & 82.96\% & 919.53 & 229.883 & 29.42 & 0.000 & \\ A (m\({}^{2}\)) & 1 & 2.94\% & 187.16 & 187.164 & 23.95 & 0.000 & 1.17 \\ M (\%) & 1 & 4.65\% & 99.35 & 99.350 & 12.72 & 0.001 & 3.47 \\ WS (m/s) & 1 & 5.55\% & 0.51 & 0.510 & 0.07 & 0.799 & 4.63 \\ AT (\({}^{\circ}\)C) & 1 & 69.82\% & 635.11 & 635.111 & 81.28 & 0.000 & 4.02 \\ Square & 4 & 3.56\% & 242.56 & 60.641 & 7.76 & 0.000 & \\ A\(\times\)A & 1 & 0.25\% & 20.84 & 20.844 & 2.67 & 0.106 & 1.00 \\ M\(\times\)M & 1 & 2.41\% & 0.49 & 0.492 & 0.06 & 0.802 & 7.65 \\ WS\(\times\)WS & 1 & 0.29\% & 82.98 & 82.979 & 10.62 & 0.002 & 5.41 \\ AT\(\times\)AT & 1 & 0.62\% & 21.36 & 21.358 & 2.73 & 0.102 & 1.79 \\
2-Way & 6 & 4.88\% & 411.90 & 68.649 & 8.79 & 0.000 & \\ A\(\times\)M & 1 & 0.01\% & 3.74 & 3.743 & 0.48 & 0.491 & 1.13 \\ A\(\times\)WS & 1 & 0.02\% & 5.00 & 4.998 & 0.64 & 0.426 & 1.25 \\ A\(\times\)AT & 1 & 0.27\% & 22.67 & 22.665 & 2.90 & 0.092 & 1.06 \\ M\(\times\)WS & 1 & 0.05\% & 3.90 & 3.897 & 0.50 & 0.482 & 3.79 \\ M\(\times\)AT & 1 & 3.54\% & 301.16 & 301.160 & 38.54 & 0.000 & 2.11 \\ WS\(\times\)AT & 1 & 0.99\% & 83.65 & 83.647 & 10.71 & 0.001 & 4.18 \\ Error & 93 & 8.60\% & 726.67 & 7.814 & & & \\ Total & 107 & 100.00\% & 8447.17 & & & & \\ \hline Model Summary & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \end{tabular} \begin{tabular}{l c c c c} \hline \hline Model & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} \begin{tabular}{l c c c} \hline \hline Model & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} \begin{tabular}{l c c c} \hline \hline Model & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} \begin{tabular}{l c c c} \hline \hline Model & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} \begin{tabular}{l c c} \hline \hline Model & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} \begin{tabular}{l c c} \hline \hline Model & & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\ \end{tabular} \begin{tabular}{l c c} \hline \hline Model & & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}  \ \begin{tabular}{l c c} \hline \hline Model & & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular} 
\begin{tabular}{l c c} \hline \hline Model & &Figure 5, Pareto chart and residual diagnostics for Coniferous Plants. The Pareto chart highlights AT, M, and the interaction term M\(\times\)AT as the most significant predictors of T variation. The normal probability plot shows a close alignment to the theoretical distribution, indicating normality. The Versus Fits and Versus Order plots exhibit randomly scattered residuals without obvious patterns, confirming the assumptions of independence and constant variance (homoscedasticity) and validating the statistical reliability of the model.