Statistics & Probability

Instant, private, and free

Pearson Correlation Calculator.

Calculate Pearson's r and r² for paired data.

On-device calculationNo signup
01

Set your values

Results update as you type.

correlation: 0.852803

correlation

0.852803
rSquared: 0.727273

rSquared

0.727273

Results update automatically as you type.

Use Cases

Analyze relationship between two variables

Use this calculator to quickly determine if two variables move together linearly, such as study hours and test scores, or temperature and ice cream sales.

Example: Enter hours studied (X) and test scores (Y) for several students to see if more study time correlates with higher scores.

Validate assumptions for further analysis

Before running regression or other statistical tests, check the correlation between variables to ensure a linear relationship exists.

Example: Check if advertising spend (X) correlates with revenue (Y) before building a predictive model.

Frequently Asked Questions

What does Pearson's r tell me?
Pearson's r measures the strength and direction of a linear relationship between two variables. Values range from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no linear correlation.
How do I enter data in the calculator?
Enter your paired X values and Y values in the corresponding fields. Each pair should be aligned by position (e.g., first X with first Y). The calculator will compute r based on the pairs you provide.
What if my data has missing pairs?
The calculator requires paired data. If a value is missing for either X or Y in a pair, that pair is typically excluded from the calculation. Ensure each pair has both values for accurate results.

Tips & Common Mistakes

Tips

  • Ensure your X and Y values are paired correctly: the first X corresponds to the first Y, and so on.
  • Use at least 5-10 pairs for a more reliable correlation estimate.
  • Pearson's r only measures linear relationships; consider other methods if the relationship is non-linear.
  • Outliers can heavily influence r; check your data for extreme values.

Common Mistakes to Avoid

  • Mixing up the order of pairs, which can lead to incorrect r values.
  • Using non-numeric data or leaving blank cells, which may cause errors or exclude pairs.
  • Interpreting correlation as causation; a high r does not mean one variable causes the other.

Last updated: August 13, 2026