Correlation Heatmap

Use Cases

The Correlation Heatmap is designed for exploring the relationships among many parameters at the same time. It can help you discover unexpected correlations that can point to root causes and identify how changes to input fields may affect output fields. The Correlation Heatmap is most helpful early in the exploratory process; after you have identified pairs of interest, you can investigate these pairs in more detail with some of the other tools, such as the Curve Fit Analysis.


The Correlation Heatmap is a multivariate analysis that you can perform on continuous fields on the Cycle and Part models. You can select multiple machines of a given type when performing the analysis on the Cycle model. Using the Cycle model, the Correlation Heatmap can investigate parameters from the same machine type with high granularity. Using the Part model lets you compare fields across different machines, but with lower granularity (the Part model is limited by the serial number or other identifier used to construct the data model).


The Correlation Heatmap is an exploratory tool that allows you to investigate the pairwise relationships among up to 10 parameters by computing the Pearson-R correlation coefficient for each pair. This tool also presents a table of summary statistics for each selected parameter that includes Count, Standard Deviation, Minimum, and Maximum.