## 4 Results and Analysis
### Questionnaire Data Results and Analysis
#### 4.3.4 Multiple Linear Regression Results and Analysis  
Multiple linear regression was used to verify common visual elements affecting identity psychology in landscape architectures seen from different urban observation decks. The dependent variable was \"city identity sense\", while the independent variables were the data of eight visual elements and fifty-six corresponding visual aesthetic evaluation indicators. Model summary, ANOVA results, and coefficient results (Table 6) were obtained, yielding the multiple regression equation.  
\[\text{Y}=0.573\times\text{x}_{1}+0.316\times\text{x}_{2}+0.515\times\text{x}_ {3}+0.169\times\text{x}_{4}+0.617\times\text{x}_{5}+0.223\times\text{x}_{6}+0.799 \times\text{x}_{7}+0.094\times\text{x}_{8}+1.021\times\text{x}_{9}-3.10\]  
Based on the data from the overall regression model presented in the table, the following results can be derived.  
1. \(\text{F}=14.251\) and \(p\)-value = 0.009 < 0.05, which is less than 0.05. This indicates that the model is significant at the 0.05 level. It passes the F-test and has a linear correlation with the dependent variable. Hence, the model is validated.
2. The adjusted R\({}^{2}\) considers the number of predictors used to forecast the target variable, helping to decide if adding new variables improves the model fit. Here, the adjusted R\({}^{2}\) value was 0.726. This implies that the nine influencing factors could account for 72.6% of the variation in identity psychology. In other words, 72.6% of the attachment emotion resulted from these nine factors, suggesting that the model had a moderate fit.
3. The Durbin-Watson (D-W) value = 2.006; since this value is close to 2, it implies that there is no autocorrelation present. As a result, the data used in the model are independent.  
Based on the significance and correlation coefficients of the independent and dependent variables, the following conclusions can be reached.  
1. Among the independent variables, the color brightness of green plants (\(p=0.000<0.01\), B = 1.021), the color vividness of mountains (\(p=0.000<0.01\), B = 0.799), the color vividness of water (\(p=0.000<0.01\), B = 0.617), the color vividness of landmarks (\(p=0.000<0.01\), B = 0.573), the color brightness of skyscraper buildings (\(p=0.000<0.01\), B = 0.515), the color brightness of high-rise buildings (\(p=0.001<0.05\), B = 0.316), the proportional coordination of mountains (\(p=0.001<0.01\), B = 0.223), and the morphological richness of green plants (\(p=0.007<0.01\), B = 0.094) were prominent. There was a significant positive correlation between the evaluation factors of these nine visual elements and identity psychology.
2. The correlation coefficient of the decorative delicacy evaluation is less than 0, but the \(p\)-value is more than 0.05, and the correlation coefficient is less than 0.3; therefore, although there is a negative correlation between decorative delicacy and the identity of place of each visual element, the influence is small. Overall, the multiple linear regression results are consistent with the correlation analysis, and Hypothesis 3 is verified.