Pearson Correlation Coefficient Calculator

Enter your two data sets to measure the linear relationship.

Provide the data for the first variable (X), separated by commas. Must have the same number of values as Y.

Examples

Explore how correlation works with different data sets.

Strong Positive Correlation

positive

As X increases, Y tends to increase.

X Values: 1, 2, 3, 4, 5, 6

Y Values: 2, 3.1, 3.9, 5.2, 6, 7.1

Strong Negative Correlation

negative

As X increases, Y tends to decrease.

X Values: 10, 20, 30, 40, 50

Y Values: 88, 70, 65, 50, 32

No Correlation

none

No clear linear relationship between X and Y.

X Values: 1, 2, 3, 4, 5, 6, 7

Y Values: 10, -5, 12, 0, 15, -8, 7

Other Titles
Understanding the Pearson Correlation Coefficient
A guide to measuring the linear relationship between two variables.

1. What is the Pearson Correlation Coefficient (r)?

The Pearson Correlation Coefficient, denoted 'r', measures the strength and direction of a linear relationship between two variables. It produces a value between -1 and +1.

2. Interpreting the Results

R-squared (Coefficient of Determination)
R-squared (r²) is the proportion of the variance in the dependent variable that is predictable from the independent variable. For example, an r² of 0.64 means that 64% of the variance in Y can be explained by X.
t-statistic and p-value
The t-statistic and p-value are used to test the hypothesis that the correlation is significantly different from zero. A small p-value (typically < 0.05) indicates that the observed correlation is statistically significant.