Home / Statistical Tools / Analysis Tools / Correlation and Covariance / Correlation and Covariance
Correlation and Covariance¶
Measure the relationship between two or more numeric variables using Pearson, Spearman, or Covariance.
What is Correlation and Covariance?¶
Correlation and covariance analyses quantify the strength and direction of relationships between numeric variables. Quantum XL produces a matrix showing each pair of selected columns, with coefficients and (for correlations) p-values for statistical significance.
Three Analysis Types¶
Three analysis types are available, each selected from a separate item in the Excel ribbon dropdown:
| Type | When to Use |
|---|---|
| Pearson Correlation | Linear relationships between numeric variables. Assumes data is approximately normally distributed. |
| Spearman Correlation | Non-linear but monotonic relationships, ordinal data, or non-normal data. Uses ranks instead of raw values. |
| Covariance | Unstandardized measure of how variables move together. Useful when magnitudes matter (e.g., portfolio analysis). Shows variance on the diagonal. |
When to Use¶
- When you want to measure the strength of relationships between numeric variables
- When you need a matrix view of pairwise relationships across many columns
- When you want p-values to assess statistical significance of correlations
- When you need covariance values for portfolio or risk analysis
Example Output¶
A typical correlation output shows a matrix with each row and column representing one of your data columns. Cells show the coefficient (and p-value for Pearson/Spearman). Significant p-values (below 0.05) are highlighted in red.
Learn More¶
- Examples — Step-by-step tutorials for each analysis type
- Preparing Your Data — Data types, column requirements, and the behavior matrix