Math & Statistics

Correlation Calculator

Measures how strongly two columns move together, with Pearson and Spearman side by side.

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How to use this tool

  1. Paste two numbers per line, separated by a comma or a tab — a two-column paste from a spreadsheet works as is.
  2. Select Correlate.
  3. Compare Pearson r with Spearman rho. A much higher Spearman means the relationship is real but not straight.
  4. Check the pair count before quoting anything — a strong r on five pairs is not evidence of much.

What correlation calculator does

Pearson r measures how close the relationship is to a straight line. Spearman rho measures whether one variable rises with the other at all, straight line or not. Reporting both is the point: when they agree the relationship really is linear, and when Spearman is much the higher of the two the relationship is real but curved, which is the case where a linear model quietly underperforms.

Paste two numbers per line, separated by a comma or a tab, so a two-column copy out of a spreadsheet goes straight in. The output includes R squared, the best-fit line, and a plain-English strength reading. It does not include a causal claim, and neither should any correlation — a strong r says the two move together and says nothing whatever about which one moves the other.

Frequently asked questions

Pearson measures how close the points lie to a straight line. Spearman throws away the values and keeps only their rank order, so it measures whether one variable rises with the other in any consistent way. A curved but perfectly consistent relationship gives a Spearman near 1 and a lower Pearson.

No, and no correlation coefficient can tell you that. Two variables can move together because one drives the other, because something else drives both, or by coincidence in a small sample. The number measures association only; the causal claim has to come from how the data was collected.

It is Pearson r squared, and it reads as the share of the variation in one variable that the straight-line fit accounts for. An r of 0.7 sounds strong but gives an R squared of 0.49, meaning about half the variation is still unexplained — which is why R squared is the more sobering of the two figures.