Statistics

Pearson Correlation Calculator

Calculate Pearson r and R² from paired X and Y datasets.

Last validated: 2026-02-14

Pearson Correlation Calculator measures linear association strength between two numeric variables. It is useful for exploratory analysis, feature screening, and relationship checks. The tool returns correlation coefficient and significance estimates where applicable. Use it to identify potential linear patterns before modeling.

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Input Pattern

Enter values in the left panel, keep units explicit, run the calculation, then copy or share the result. Invalid fields are highlighted immediately.

Correlation Inputs

Result

r: 0.774597

R²: 0.600000

How to use this tool

Enter paired x and y data values.
Run the calculation to compute Pearson r.
Check sign and magnitude to assess direction and strength.

Worked Example

Auto-generated from the tool's current default or entered inputs.

Example Inputs

X values: 1,2,3,4,5
Y values: 2,4,5,4,5
R: 0.7745966692414834
R squared: 0.6000000000000001

Expected Outputs

R: 0.774597
R squared: 0.6

Interpretation

R is 0.7745966692414834. Use this as a decision input, not as a standalone conclusion.
R squared is 0.6000000000000001. Use this as a decision input, not as a standalone conclusion.
Interpret this value alongside sample quality and assumptions to avoid overconfidence.

Scenario Compare (A vs B)

Use this to compare two input sets and quantify change in key outputs.

Confidence and limitations

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

Formula References

Frequentist estimation and hypothesis-test formulas used in standard textbooks.
Interpretation depends on sample quality and assumptions.

Assumptions

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

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