## 3 Results
The temporal pattern of the ONI and air temperature, representing the average monthly temperature in Kuching City from 1988 to 2019, is shown in Figure 5. As depicted in Figure 6, a positive relationship between the ONI and monthly temperature from MMD is observed. From May 1988 to May 1989, the ONI values ranged from \(-\)1 to \(-\)1.8, resulting in a decrease in temperature from 24.8 \({}^{\circ}\)C to 24 \({}^{\circ}\)C. Negative ONI values indicate the occurrence of La Nina events, observed in December 1999, 2007, and 2010. In contrast, during El Nino events spanning from June 1997 to April 1998, the recorded ONI was 0.88, leading to an increase in temperature from 26.48 \({}^{\circ}\)C to 27.9 \({}^{\circ}\)C. A similar phenomenon recurred from late 2015 to March 2016. El Nino events also occurred in 1991, 1997-1998, and 2015-2016 [13; 14; 15]. To further understand the relationship between independent and dependent variables, this study employs a linear regression model. This model helps elucidate changes in the dependent variable using an independent variable. According to [PERSON] [30], the linear regression model is regarded as the most stable approach in scientific research. Table 3 presents the results of the linear regression analysis, aiming to comprehend the impact of ENSO on the air temperature obtained from the Meteorological Department in Kuching City, Sarawak. Figure 5: ONI time series and air temperature during 1988\(-\)2019. Figure 6: Correlation analysis between ONI and air temperature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R** & **R2** & **Adjusted R2** & **The Standard Error for the Estimation** \\ \hline
1 & 0.866 & 0.75 & 0.75 & 0.40861 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the linear regression analysis of the impact of ENSO on air temperature obtained from the MMD in Kuching City, Sarawak. Predictors: (Constant) ONI Index  
Table 3 reports the coefficients of R and R2. An R-value of 0.866 indicates a high correlation, indicating a significant relationship between the ONI and air temperature from the MMD. Moreover, an R2 value of 0.75, which indicates the extent to which the total variation in the dependent variable (temperature from the MMD) can be explained by the independent variable (ONI), indicates that 75% of the temperature can be explained by the ONI. Table 4 below illustrates the extent to which the regression equations correspond to the data to understand the dependent variables. Table 4 presents the results of the linear regression model, which aims to understand the relationship between the dependent variables. In a regression analysis, the \(p\)-value is used to assess the significance of the regression model. If the \(p\)-value is less than 0.05 (\(p<0.05\)), it indicates that the regression model corresponds well to the data and is considered statistically significant. In this study, the linear regression model between the ONI parameters and temperature from the MMD yielded a \(p\)-value of 0.000. This very low \(p\)-value indicates a highly significant relationship between the ONI parameters and MMD temperature data. Therefore, our results support the robustness and reliability of the ONI and temperature data used in the analysis. Overall, our findings suggest that the linear regression model effectively explains the relationship between the ONI parameters and temperature from MMD, providing valuable insights into the impact of ENSO on temperature patterns in the study area. Table 5 shows a single regression formula for understanding the effect of the ONI on three dependent parameters to predict the impact of ENSO occurrences on air temperature and surface temperature. The table presents the adjusted R2 values, which indicate the proportion of variation in the dependent variables that can be explained by the ONI. Based on Table 5, the highest adjusted R2 value is observed for the monthly average temperature from the MMD, with a coefficient of 0.75.