Find the Pearson r between any two variables instantly
Enter Paired Data
Separate values with commas, spaces, or new lines. X and Y must have the same number of values.
Results
The two variables move almost perfectly together.
Data pairs (10)
| # | X | Y | (x−x̄)(y−ȳ) |
|---|---|---|---|
| 1 | 2 | 3 | 50.16 |
| 2 | 4 | 5 | 25.76 |
| 3 | 5 | 6 | 16.56 |
| 4 | 7 | 8 | 4.16 |
| 5 | 8 | 9 | 0.96 |
| 6 | 10 | 9 | -0.24 |
| 7 | 12 | 11 | 3.36 |
| 8 | 14 | 13 | 14.96 |
| 9 | 16 | 15 | 34.56 |
| 10 | 18 | 17 | 62.16 |
How it works
A correlation coefficient calculator computes the Pearson r — the standard measure of linear association between two variables. Paste your paired X and Y values, separated by commas or new lines, and get the correlation coefficient, R², direction, and strength interpretation in one click. Useful for statistics homework, data analysis, and research validation.
Pearson r ranges from −1 to +1. A value of +1 means a perfect positive linear relationship (as X rises, Y rises proportionally), −1 means a perfect negative relationship, and 0 means no linear association. Values around ±0.7 or higher are generally considered strong.
You need at least 3 paired data points to compute a meaningful correlation. In practice, 10 or more pairs give a more reliable result, since small samples can produce misleadingly high r values by chance.
r (Pearson r) measures the strength and direction of the linear relationship. R² (r-squared) is simply r multiplied by itself and represents the proportion of variance in Y that is explained by X. For example, r = 0.9 gives R² = 0.81, meaning X explains 81% of the variation in Y.
No. Correlation only measures the degree to which two variables move together linearly. A high r value does not prove that changes in X cause changes in Y — a third variable (confound) or coincidence could explain the relationship.