Correlation¶

Measures how strongly variables move together, as a correlation matrix with significance and (where defensible) confidence intervals.

When to use it¶

To quantify the association between two or more variables — for example, whether higher scores on one item go with higher scores on another.

Variables — two or more columns for the main matrix.
Control variables (optional) — turns the analysis into a partial correlation, holding these constant (Pearson or Spearman only).
Second variable set (optional) — produces a rectangular cross matrix of the first set against the second, instead of a square matrix.

Coefficient: Pearson (linear), Spearman and Kendall / Kendall tau-c (rank-based), Phi / Tetrachoric (binary), Polychoric (ordinal).
Table: Compact vs full layout; Confidence intervals (95%, via the Fisher-z transform for Pearson and Spearman); Number columns for wide matrices.
Verbal report: How much plain-language interpretation to write, as a single dropdown — None, Key findings (only strong correlations), Significant only, or Full (every pair). The amount of prose scales with how much there is to say.
Plots: Heatmap of the matrix, and pairwise scatter plots with a regression line and its standard-error band. Plot only significant correlations trims the pairwise plots. The heatmap’s color scale is selectable in its plot settings (a diverging map such as bwr or RdBu by default, plus sequential options like viridis and Blues).

The correlation matrix with coefficients, significance, degrees of freedom, and optional CIs.
A verbal report describing each association.
Optional heatmap and scatter figures.

Confidence intervals are shown only where they are statistically defensible — Pearson and Spearman. Other coefficients show no CI.
A column with no variance (a single repeated value) has an undefined correlation; its cells are left blank and a note explains why.
Choosing Pearson on ordinal data triggers a warning, since rank-based coefficients are usually more appropriate there.