## 3 Results and Analysis
### Data Analysis Results
#### 3.2.3 Univariate SLR Modelling  
Univariate SLR modelling involved using the SR-BJPA, T, W/B ratio, and #days, one by one, as the independent (predictor) variable, and CS, as the dependent variable. Thus, four models were established.  
Figure 5: The CS development trends in all samples with W/B ratio 0.6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Matches** & **Pearson Correlation Coefficient** & **Remark** \\ \hline SR-BJPA versus CS & \(-\)0.62277 & Strong Neg. \\ T versus CS & 0.17226 & Nearly Weak Positive \\ W/B Ratio versus CS & \(-\)0.34249 & Weak Negative \\ \#days versus CS & 0.580522 & Nearly Strong Positive (Pos.) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Correlations between every independent variable and CS.  
The first model involved using SR-BJPA as the independent variable, as follows:  
\[CS=52.978+(-81.198)\times SR\_BJPA \tag{1}\]  
The SLR model is highly significant (_p_ = 2.2 \(\times\) 10\({}^{-16}\); the relationship that CS is 52.978 MPa plus SR\_BJPA (within the [0,1] scale) multiplied by \(-81.198\) does exist), and the data-model fitness (i.e., the explanatory power of the model) is acceptable (\(R^{2}\) = 0.3791). Thus, the predictive effectiveness of this model can be asserted.  
The second model involved using T as the independent variable, as follows:  
\[CS=36.33259+0.12179\times T \tag{2}\]  
The SLR model is significant (_p_ = 0.003916 \(<\) 0.05; the relationship that CS is plus the curing temperature (in the Celsius scale) multiplied by 0.12179 does exist), but the data-model fitness is unacceptable (\(R^{2}\) = 0.03063). Thus, the predictive effectiveness of this model is model is questionable (i.e., there is excessive unexplained variance in the data).  
The third model involved using the W/B ratio as the independent variable, as follows:  
\[CS=73.191+(-61.537)\times WB\_Ratio \tag{3}\]  
This SLR model is highly significant (_p_ = 2.234 \(\times\) 10\({}^{-9}\); the relationship that CS is 73.191 MPa plus the W/B ratio multiplied by \(-61.537\) does exist), but the data-model fitness is quite poor (\(R^{2}\) = 0.1219). Thus, the predictive effectiveness of this model is moderate (i.e., there is substantial unexplained variance in the data).  
The fourth model involved using #days as the independent variable, as follows:  
\[CS=32.4467+0.1963\times\#Days \tag{4}\]  
The SLR model is highly significant (_p_ = 2.2 \(\times\) 10\({}^{-16}\); the relationship that CS is 32.4467 MPa plus the #days multiplied by 0.1963 does exist), and the data-model fitness is acceptable (\(R^{2}\) = 0.3513). Thus, the predictive effectiveness of this model can be asserted. The \(R^{2}\) value will climb to 0.3986 if #days are logged, according to the rule in Section 3.2.1.  
Thus, using SR-BJPA or #days as the sole predictor for the CS of samples is acceptable, whereas the effectiveness of using the W/B ratio to predict CS is questionable. These claims can be roughly mapped to the strength of the positive/negative correlations (i.e., \(|r|\)) presented in Table 9. Table 10 provides a summary of the four established models. The justification criteria for \(p\) are applied normally, whereas those for \(R^{2}\) follow a recent statistical study [33]. Hence, we built additional bivariate MLR models to determine whether using two variables together can lead to better results.