## 4 Discussion
Increasing in education index (\(X_{4}\)) will reduce the average number of infant deaths (\(Y_{1}\)), increase the average number of under-5 deaths (\(Y_{2}\)) and increase the average number of maternal deaths (\(Y_{3}\)). Moreover, increasing the percentage of households with improved sanitation (\(X_{5}\)) will reduce the average number of infant deaths (\(Y_{1}\)), reduce the average number of under-5 deaths (\(Y_{2}\)) and increase the average number of maternal deaths (\(Y_{3}\)). However, \(X_{5}\) does not significantly affect the number of maternal deaths (\(Y_{3}\)). Table 6 shows the differences between correlation and GWMPIGR modelling for Surabaya City. Some predictors have a consistent relationship with correlation, and some do not. As we can see that the correlation between the number of under-5 deaths (\(Y_{2}\)) and education index (\(X_{4}\)) or the percentage of household that has improved sanitation (\(X_{5}\)) is not the same as that observed in the GWMPIGR modelling. Additionally, the correlation between the number of under-5 deaths (\(Y_{3}\)) and the percentage of the population with BPJS health insurance (\(X_{3}\)) is not consistent with the GWMPIGR modelling. However, \(X_{3}\) shows that it is not significantly correlated with \(Y_{3}\) and that it does not significantly affect \(Y_{3}\). However, the pattern of the relationship between the response variable and the predictor variable is different for each location. Furthermore, the results of the parameter testing for the GWMPIGR model 1 produced several regional groups based on significant predictors, which are presented in Table 7.  
There are three regional groups according to the significant predictors for the number of infant deaths and the number of under-5 deaths, while there are two regional groups based on significant predictors for the number of maternal deaths. For the number of infant deaths (\(Y_{1}\)), 16 districts/cities in East Java are affected by all of the predictor variables, 21 districts/cities are influenced by the percentage of active integrated service post (\(X_{1}\)), the percentage of active family planning participants (\(X_{2}\)), the percentage of the population with BPJS health insurance (\(X_{3}\)), education index (\(X_{4}\)) and only one district/city is affected \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Response Variable**} & \multirow{2}{*}{**Predictors**} & \multirow{2}{*}{**Cor (\(Y1\)/\(q\), \(X\))**} & \multicolumn{6}{c}{**GWMPIGR Modeling**} \\ \cline{4-9}  & & & & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) \\ \hline \multirow{4}{*}{\(Y_{1}\)} & \(X_{1}\) & + * & + * & + * & & \\  & \(X_{2}\) & + & & & + * & & \\  & \(X_{3}\) & + & & & & + * & \\  & \(X_{4}\) & - * & & & & & \\  & \(X_{5}\) & - & & & & & \\ \hline \multirow{4}{*}{\(Y_{2}\)} & \(X_{1}\) & - * & - * & - * & & & \\  & \(X_{2}\) & - & & & - * & - * & \\ \cline{1-1}  & \(X_{3}\) & - & & & & & \\ \cline{1-1}  & \(X_{4}\) & - & & & & & \\ \cline{1-1}  & \(X_{5}\) & + & & & & & \\ \hline \multirow{4}{*}{\(Y_{3}\)} & \(X_{1}\) & + & + * & + * & & & \\ \cline{1-1}  & \(X_{2}\) & - & & & & & \\ \cline{1-1}  & \(X_{3}\) & - & & & & & \\ \cline{1-1}  & \(X_{4}\) & + & & & & & \\ \cline{1-1}  & \(X_{5}\) & + * & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of signs between correlation and regression parameters coefficient of the GWMPIGR model for Surabaya City.