Overall Model System
The Overall Model System output item, which is generated by default, displays a graphical representation of the causal relations between series in the model system. By default, relations for the top 10 models are shown, as determined by the value of the R Square fit statistic. The number of top models (also referred to as best-fitting models) and the fit statistic are specified on the Series to Display settings (on the Build Options tab) of the Temporal Causal Modeling dialog.
The Overall Model System item contains interactive features. To enable the features, activate the item by double-clicking the Overall Model System chart in the Viewer. In this example, it is most important to see the relations between all series in the system. In the interactive output, select All series from the Highlight relations for drop-down list.
All lines that connect a particular target to its inputs have the same color, and the arrow on each line points from an input to the target of that input. For example, Lever3 is an input to KPI_19.
The thickness of each line indicates the significance of the causal relation, where thicker lines represent a more significant relation. By default, causal relations with a significance value greater than 0.05 are hidden. At the 0.05 level, only Lever1, Lever3, Lever4, and Lever5 have significant causal relations with the key performance indicator fields. You can change the threshold significance level by entering a value in the field that is labeled Hide links with significance value greater than.
In addition to uncovering causal relations between Lever fields and key performance indicator fields, the analysis also uncovered relations among the key performance indicator fields. For example, KPI_10 was selected as an input to the model for KPI_2.
You can filter the view to show only the relations for a single series. For example, to view only the relations for KPI_19, click the label for KPI_19, right-click, and select Highlight relations for series.
This view shows the inputs to KPI_19 that have a significance value less than or equal to 0.05. It also shows that, at the 0.05 significance level, KPI_19 was selected as an input to both KPI_18 and KPI_7.
In addition to displaying the relations for the selected series, the output item also contains information about any outliers that were detected for the series. Click the Series with Outliers tab.
Three outliers were detected for KPI_19. Given the model system, which contains all of the discovered connections, it is possible to go beyond outlier detection and determine the series that most likely causes a particular outlier. This type of analysis is referred to as outlier root cause analysis and is covered in a later topic in this case study.