Statistics & Probability

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Correlation Calculator.

Calculate Pearson correlation for paired observations.

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Set your values

Results update as you type.

Pearson correlation: 0.990148

Pearson correlation

0.990148

Results update automatically as you type.

Use Cases

Analyze relationship between two variables

Use this calculator to quickly assess whether two variables move together, such as height and weight, or study hours and test scores.

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

Check data for linearity before further analysis

Before running regression or other statistical tests, use the correlation coefficient to confirm a linear relationship exists.

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

Frequently Asked Questions

What does the correlation coefficient tell me?
The correlation coefficient (r) measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1. Values near 1 indicate a strong positive relationship, near -1 a strong negative relationship, and near 0 little to no linear relationship.
How do I enter my data?
Enter your paired values in the X values and Y values fields. Each field should contain a list of numbers, typically separated by commas or spaces. Ensure both lists have the same number of values; otherwise, the calculator may not compute correctly.
What is the formula used?
The calculator uses the Pearson correlation coefficient formula: r = Σ((x - x̄)(y - ȳ)) / sqrt(Σ(x - x̄)² * Σ(y - ȳ)²), where x̄ and ȳ are the means of the X and Y values respectively.

Tips & Common Mistakes

Tips

  • Ensure your X and Y lists have the same number of values; mismatched lengths will cause errors.
  • Use consistent units for each variable, but note that correlation is unitless and unaffected by scaling.
  • Outliers can heavily influence the correlation coefficient; consider removing or investigating them.
  • Remember correlation does not imply causation; a strong correlation does not mean one variable causes the other.

Common Mistakes to Avoid

  • Entering unequal numbers of X and Y values, which prevents calculation.
  • Including non-numeric characters or empty entries in the data fields.
  • Interpreting a correlation close to 0 as 'no relationship' when it may be non-linear.

Last updated: August 13, 2026