CORREL Function

Excel Functions › Statistical

All versions Statistical

The Excel CORREL function returns the correlation coefficient between two data sets — a number from -1 to 1 that measures how strongly the two variables move together.


Quick answer:
=CORREL({3,2,4,5,6},{9,7,12,15,17}) ~0.997 (strong positive)

Syntax

=CORREL(array1, array2)
ArgumentDescription
array1RequiredThe first set of values.
array2RequiredThe second set of values — the same length as array1.

How to use it

CORREL measures the strength and direction of a linear relationship. The result, the Pearson correlation coefficient, ranges from -1 (perfect inverse) through 0 (no linear relationship) to +1 (perfect positive).

=CORREL({3,2,4,5,6}, {9,7,12,15,17}) // ~0.997

The two arrays must have the same number of values; pairs containing text, logicals, or empty cells are ignored together. CORREL is symmetric — swapping array1 and array2 gives the same answer.

Correlation isn't causation, and CORREL only captures linear association — a strong curved relationship can still produce a near-zero coefficient. Plot the data to confirm what the number is telling you.

Try it: interactive demo

Live demo

Pick a CORREL example to see the formula and its result.

Result:

Practice workbook

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Frequently asked questions

What range of values can CORREL return?
From -1 to +1. A value near +1 means a strong positive linear relationship, near -1 a strong negative one, and near 0 little or no linear relationship.
Is CORREL the same as PEARSON?
Yes — CORREL and PEARSON compute the identical Pearson correlation coefficient and return the same result. CORREL is the more commonly used name.
Does correlation imply causation?
No. A high correlation shows two variables move together but does not prove one causes the other — a third factor or coincidence can drive the relationship.
Why might CORREL return near zero for clearly related data?
CORREL only measures linear association. A strong but curved (non-linear) relationship can yield a coefficient close to zero, so always inspect a scatter plot.

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