Pearson's r is a number between -1 and +1 that measures how closely two variables follow a straight-line relationship. Values near ±1 indicate a strong relationship; values near 0 indicate little or no linear relationship.

What does r-squared mean?

r-squared (r²) tells you what proportion of the variance in Y can be explained by X. For example, r = 0.8 gives r² = 0.64, meaning 64% of the variation in Y is explained by the linear relationship with X.

Does correlation imply causation?

No. A high correlation only means the two variables tend to move together; it does not prove that one causes the other. Always consider confounding variables and domain knowledge.

How many data points do I need?

You need at least 2 pairs, but meaningful results typically require at least 10–30 pairs. With very few points, even a high r can arise by chance.

What correlation strength is considered strong?

Common thresholds: |r| 0.7 = strong. These are guidelines — context matters; a 0.5 correlation may be very meaningful in social science but weak in physics.

Can I use this for non-linear relationships?

Pearson's r only measures linear relationships. If your scatter plot shows a curved pattern, consider Spearman's rank correlation instead, which handles monotonic but non-linear relationships.

Correlation Coefficient Calculator

Enter two comma-separated datasets and instantly see Pearson's r, r-squared, and the strength of their linear relationship.

Enter Datasets

Dataset X (comma-separated)

Dataset Y (comma-separated)

Quick Examples

Enter two datasets and click Calculate to see the correlation.

Pearson's r

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r-squared (r²)

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Correlation Strength

-1 (perfect negative) 0 (none) +1 (perfect positive)

Descriptive Statistics

Statistic	X	Y

Scatter Plot

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Summary

Enter two comma-separated datasets and instantly see Pearson's r, r-squared, and the strength of their linear relationship.

How it works

Enter dataset X values as comma-separated numbers (e.g. 1, 2, 3, 4, 5).
Enter dataset Y values in the same order and with the same count.
Click Calculate — the tool validates your inputs and checks for equal lengths.
Pearson's r is computed using the standard formula: sum of (xi - x̄)(yi - ȳ) divided by the product of their standard deviations.
r-squared is displayed alongside a plain-English interpretation of the correlation strength.
A scatter plot is drawn using Chart.js so you can visually confirm the relationship.

Use cases

Determine whether two financial variables (e.g. sales and ad spend) are linearly related.
Validate research hypotheses by checking correlation between measurements.
Explore relationships between student test scores across two subjects.
Assess whether temperature and ice cream sales move together.
Check if workout frequency correlates with weight loss.
Analyze survey responses across two questions for patterns.
Verify data quality by spotting unexpected correlations between columns.
Teach or learn statistics concepts with a live, visual calculator.

Frequently Asked Questions

Last updated: 2026-09-22 · Reviewed by Nham Vu