The following correlations were detected:
| Pearson’s coefficient | Significance | |
| Measles immunization and age | 0.430 |
0.000 |
| Measles immunization and age squared | 0.325 |
0.000 |
| Measles immunization and low edu | -0.081 |
0.018 |
| Height and low education | -0.191 |
0.000 |
| Height of respondent and sex of the child | 0.080 |
0.023 |
| WAZ and age | -0.191 |
0.000 |
| WAZ and age squared | -1.330 |
0.000 |
| WAZ and sex | 0.077 |
0.042 |
| WAZ and measles immunization | -0.186 |
0.000 |
| WAZ and low education | -0.182 |
0.000 |
| WAZ and height of respondent | 0.210 |
0.000 |
All correlations show a both the size and direction of the association. The correlations (see the first column- Pearson’s correlation coefficient) are each in the direction expected, with the exception of WAZ and measles immunization. This reason was looked at in detail in the two-way analysis, which showed that age is a confounding factor for measles and WAZ. The solution to uncovering the true relationship is to break the analysis group into smaller age groups for the analysis. We can begin to see where we will need to watch for collinearity between certain independent variables, such as mother's height and low education.
It is also helpful to visualize these linear relationships. In order to see the graph of each variable set, the scatterplot function can be used.