From Grapheme wiki
In statistics, Covariance is a measure describing the joint variability between two sets of random variables (i.e. how two variables vary together). A positive covariance indicate that two variables are positively related, while a negative covariance indicate that the variables are inversely related. Note that the covariance of a variable with itself is the variance of the variable.
Covariance is often used to assess associations between economic and financial data such as stock returns and interest rates.
More details on Covariance analysis and related significance tests can be found here:
To perform a Covariance analysis in Grapheme, click on Create New Analysis from the toolbar of the Statistical Analysis View. Assign a name to the panel and select “Covariance Analsysis” from the list. Click on Next.
In the Sources tab, select the Source Table from the ones available in the Tables view, select the view of the table and the columns you want to include in the correlation analysis. Click on Next.
In the Configuration tab, select whether or not to use Bias Correction and and Click on Finish.
- All the data available in the panel are updated on the fly, so that any change in the table values, is immediately reflected by the panel tables and charts. Automatic update can be temporary suspended, by clicking on the lock button in the main panel toolbar.
- All the data contained in each table, can be copied to the clipboard for further reporting by clicking on the button available in the toolbar.
- Each chart can be exported as Image by clicking on the “Save as Image” button available in the toolbar