## 3 Results
### Correlation Analysis: Key Variables
There was a statistically significant negative correlation between PCS and the number of sources to cause thermal discomfort (\(r=-0.100\), \(p<0.05\)), age (\(r=-0.234\), \(p<0.01\)), and smoking status (\(r=-0.184\), \(p<0.01\)), highlighting the adverse effects of ageing and smoking on physical health. Furthermore, a significant negative correlation (\(p<0.05\)) was found between the duration of the residence and mental health component (\(r=-0.103\)), and a positive correlation with overall thermal (\(r=0.105\), \(p<0.05\)) and air quality (\(r=0.155\), \(p<0.01\)). Overall thermal satisfaction was strongly correlated with overall air quality satisfaction (\(r=0.430\), \(p<0.01\)), signifying that improvements in one aspect of the indoor environment might positively influence perceptions of the other, advocating for a holistic approach to environmental improvements. Next, overall thermal satisfaction negatively correlated with several issues that caused odour problems (\(r=-0.132\), \(p<0.01\)), suggesting the importance of addressing these problems. Lastly, the floor height level was not statistically significantly correlated with any other tested variables across all of the combinations. Effect of Environmental Satisfaction (Thermal and Air Quality) on Physical Health (PCS) and Mental Health (MCS)  
Table 4 presents the results of the three regression models that were performed to understand the effect of the independent variables on the respondents' PCS. In the linear regression models, \(B\) indicates the level of influence on the dependent variable by the independent variable, and \(SE\) means standard error. Model 1 established that overall air quality satisfaction statistically significantly predicts respondents' PCS (\(B=-0.813\), \(p<0.01\)) with an \(R^{2}\) of \(0.069\) (\(p<0.01\)). This indicates that as satisfaction with air quality decreases, PCS worsens. When housing conditions were entered into the regression Model 2, the air quality environmental variable still significantly predicted the physical health component (\(B=-0.801\), \(p<0.01\)); however, the increase in \(R^{2}\) was not significant (\(p>0.05\)). After adding the demographic and socio-economic status and housing variables, Model 3 showed the same results as Models 1 and 2 for the overall air quality satisfaction variable (\(B=-0.896\), \(p<0.01\)), as well as a predicting role of age (\(B=-0.724\), \(p<0.01\)), smoking habit (\(B=-0.953\), \(p<0.01\)), employment status (\(B=-0.259\), \(p<0.05\)) and educational level (\(B=0.418\), \(p<0.1\)) on PCS with an \(R^{2}\) value of \(0.156\) (\(p<0.01\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} \\ \cline{2-7}  & **B** & **SE** & **B** & **SE** & **B** & **SE** \\ \hline Constant & 54.478 *** & 0.918 & 54.679 *** & 1.096 & 58.250 *** & 1.759 \\ Num\_Tcontrol & 0.012 & 0.271 & 0.015 & 0.272 & \(-0.125\) & 0.265 \\ OS\_T & \(-0.016\) & 0.187 & \(-0.014\) & 0.188 & 0.094 & 0.182 \\ Num\_Tdis & \(-0.505\) & 0.369 & \(-0.503\) & 0.370 & \(-0.273\) & 0.358 \\ OS\_AQ & \(-0.813\)*** & 0.181 & \(-0.801\)*** & 0.183 & \(-0.896\)*** & 0.178 \\ Num\_Odo & 0.437 & 0.338 & 0.423 & 0.340 & 0.302 & 0.327 \\ L\_dura & & & \(-0.048\) & 0.096 & 0.039 & 0.095 \\ H\_floor & & & \(-0.007\) & 0.141 & \(-0.038\) & 0.136 \\ Age & & & & & \(-0.724\)*** & 0.172 \\ Gender & & & & & \(-0.064\) & 0.578 \\ Smo\_stat & & & & & \(-0.953\)*** & 0.305 \\ Alc\_stat & & & & & \(-0.005\) & 0.445 \\ Emp\_stat & & & & & \(-0.259\)** & 0.129 \\ Edu\_stat & & & & & 0.418 * & 0.243 \\ R\({}^{2}\) & 0.069 & & 0.070 & & 0.156 \\ AR\({}^{2}\) & & & & 0.001 & & 0.086 \\ \hline \hline \end{tabular}  
* \(p<0.1\), *
* \(p<0.05\), **  
\end{table} 
Table 4: Multiple regression analysis with PCS as outcome variable.