Correlation Calculator — Pearson r & Spearman ρ
Paste paired data to get Pearson r, r², Spearman rho and covariance, with a scatter plot and plain-language interpretation.
🔒 Runs entirely in your browser. Your input and output are never sent to a server or stored — fully private.
Scatter plot
Pearson r measures linear association; Spearman ρ uses ranks (average ranks on ties) and captures any monotonic trend. |r| below 0.3 is usually read as weak, 0.3–0.7 moderate, above 0.7 strong. Click a result to copy.
About this tool
This correlation calculator measures how strongly two variables move together. Paste your paired data — two columns, one pair per line — and it computes the Pearson correlation coefficient r, r-squared, the Spearman rank correlation, the covariance, and draws a scatter plot so you can see the pattern. Pearson catches straight-line relationships; Spearman catches any consistently increasing or decreasing one, even if it curves. It is handy for quick data exploration in research, marketing or a class project. Everything is computed in your browser, so your dataset never leaves your device.
Frequently asked questions
Does correlation prove causation?
No — this is the single most important caveat. A strong r only says two things move together; it does not say one causes the other. Ice-cream sales and drowning deaths correlate, but neither causes the other — hot weather drives both (a confounder). Correlation can come from coincidence, reverse causation, or a hidden third variable. Use it to spot relationships worth investigating, then design a proper experiment to test cause.
What is the difference between Pearson and Spearman?
Pearson's r measures a straight-line relationship and is sensitive to outliers — one extreme point can swing it a lot. Spearman ranks the values first and correlates the ranks, so it captures any monotonic relationship (consistently up or down, even if curved) and shrugs off outliers. If your scatter looks like a curved-but-rising cloud, Spearman may read high while Pearson reads lower. Comparing the two is a quick sanity check on the shape of your relationship.
Is my dataset uploaded?
No. Parsing, the coefficients and the scatter plot are all done by JavaScript in your browser, so the numbers you paste are never uploaded or stored, and it works offline once loaded. That matters for unpublished research or commercial figures. Remember correlation summarises a linear/monotonic trend only — always look at the scatter too, since very different datasets can share the same r.