This page presents an analysis of the linear relationships between several paired indicators (X,Y) in the form of Linear Correlation.

If the user chooses one of paired indicators that has a very high correlation value (positive or negative), the user will see the snapshot of scatter plot between the selected paired indicators and can perform quadrant analysis by first determining the subjective weighted values based on the user opinion.

The range of data months and regions name can be selected by the user so he can evaluate the composition of regions that are included in the strong quadrant or weak quadrant. The Color Legend of Region will support the interpretation.

There are 6 sub-menus as part of the page, as follows:

  1. The Filters: regional data level to be analyzed, names of Regions, paired X and Y indicators, and time range of data.
  2. The table that contains linear correlation values.
  3. Correlation value snapshot and its snapshot as a scatter plot.
  4. Quadrant analysis: Weighted Lines and location legend.
  5. Yearly Lag time jump from paired indicator X to indicator Y.
  6. Important notes.

Figure 1.1. Scatter Plot and Correlation screen

The steps for operating this page are:

  1. Region Selection: Specifies the level paired data (national/provincial/city-district) and specifies the areas to be analyzed (no.1 left).
  2. Paired Indicator (X, Y) Selections: Select pairs of X and Y indicators that are considered to have a linear relationship based on scientific references or case references that have occurred and published (no.1 middle and no.6)
  3. Year selection: Select the time range of paired data to be analyzed (no.1 right)
  4. Year Jump: Specifies the time jump (Lag) of paired indicator: X towards Y (no.5). For example, if Lag=2, it means that the correlation value will apply for X in 2020 to its partner Y in 2022.
  5. Pairwise Correlation: Pays attention to the paired correlation values that appear in the correlation table (no.2). A high positive correlation value will have a base shade of Green, while negatively high will have a Red shade, and if it tends to be weak near zero it will have a neutral shade (white).
  6. Pairwise Correlation Values Table: Selects one of the high correlation values (no.3 top) to analyze it in a scatter plot form and quadrant analysis (no.3 and no.4).
  7. Quadrant Analysis of Selected Paired (X,Y): Specifies weighted values of indicator X and indicator Y based on user preferences as well as the reference used (no.4 top). This action will create two reference lines on the horizontal (X) and vertical (Y) axes and will produce four areas called quadrants. The dots in the quadrant are marked based on the color of the region legend (no.4 bottom).