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Correlation Coefficient Calculator

Measure the strength and direction of the linear relationship between two variables with Pearson’s r, along with R², covariance, and a scatter plot.

Tested against reference valuesLast reviewed August 2026
Paired data
Correlation (r)0.9653
very strong positive correlation
R² (determination)
0.9318
Covariance
5.5000
n (pairs)
8
Slope
0.9167

Scatter plot

r = 0.9653
1.445.509.560.444.508.56
Data points Trend

What is the correlation coefficient?

Pearson’s correlation coefficient (r) measures the strength and direction of the linear relationship between two variables. It ranges from −1 to +1: values near +1 indicate a strong positive relationship, values near −1 a strong negative relationship, and values near 0 little or no linear relationship.

Correlation formula

Pearson’s r is the covariance of x and y divided by the product of their standard deviations:

r=(xixˉ)(yiyˉ)(xixˉ)2  (yiyˉ)2r=\dfrac{\sum (x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum (x_i-\bar{x})^2}\;\sqrt{\sum (y_i-\bar{y})^2}}

How to interpret r

|r|Strength
0.0 – 0.3Weak
0.3 – 0.7Moderate
0.7 – 0.9Strong
0.9 – 1.0Very strong
Correlation does not imply causation, and r only captures linear relationships — always look at the scatter plot.

Correlation in Excel

Use =CORREL(x_range, y_range) or =PEARSON(x_range, y_range) to get the correlation coefficient. =RSQ(y_range, x_range) gives R².

Frequently asked questions

What is the difference between r and R²?

r is the correlation coefficient, ranging from −1 to 1, showing strength and direction. R² is r squared, ranging from 0 to 1, and gives the proportion of variance in one variable explained by the other.

What does a negative correlation mean?

A negative correlation means that as one variable increases, the other tends to decrease. The closer r is to −1, the stronger that inverse relationship.

Does correlation mean causation?

No. A strong correlation shows two variables move together, but it does not prove that one causes the other — a third factor or coincidence may explain the pattern.

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