R Squared Calculator

R Squared Calculator: run r squared calculations online. Formula, assumptions, and interpretation guide.

A correlation calculator computes the Pearson correlation coefficient (r), which measures the strength and direction of the linear relationship between two continuous variables. Values range from −1 (perfect negative linear relationship) through 0 (no linear relationship) to +1 (perfect positive linear relationship). Correlation does not imply causation — two variables may be correlated because of a third confounding variable or by coincidence.

Correlation is used in finance (portfolio risk), epidemiology (disease risk factors), psychology (test validity), sports science (training load and performance), and business analytics (marketing spend and sales).

Enter paired data values for variable X and variable Y (one pair per row, or comma-separated lists of equal length).
The calculator computes the Pearson r coefficient and the coefficient of determination (r²).
Note the p-value to assess whether the correlation is statistically significant.
Inspect the scatter plot (if available) to confirm the relationship is linear — r only measures linear association.

Pearson correlation formula

r = Σ[(xᵢ − x̄)(yᵢ − ȳ)] / √[Σ(xᵢ − x̄)² × Σ(yᵢ − ȳ)²]

r² (coefficient of determination) = the proportion of variance in Y explained by X.

t-statistic for significance: t = r × √(n−2) / √(1−r²), with n−2 degrees of freedom.

Interpreting the correlation coefficient

Strength benchmarks (Cohen, 1988)

|r| of 0.10 = small effect; 0.30 = medium effect; 0.50 = large effect. An r of 0.70 means r² = 0.49 — X explains 49% of the variance in Y. Always check: is the relationship actually linear? Is it driven by outliers? Does it hold across subgroups? A high r between ice cream sales and drowning deaths simply reflects the confound of summer temperature (both rise together).

Statistics tips and best practices

Always visualise your data with a scatter plot before interpreting r — Anscombe's Quartet demonstrates four data sets with identical r but completely different relationships.
Spearman's rank correlation is more robust than Pearson's when data is ordinal or contains outliers.
Correlation between financial assets is not stable — stock correlations often spike toward +1 during market crises (diversification fails when most needed).
For time series data, be wary of spurious correlations due to shared trends — detrend first or use Granger causality tests.

The correlation between identical twins' IQ scores is approximately r = 0.86; between fraternal twins, r ≈ 0.60; between siblings, r ≈ 0.47.
In portfolio theory, diversification benefit is maximised when asset correlations are negative; in the 2008 crisis, many previously uncorrelated assets converged to r > 0.90.
SAT scores and first-year GPA correlate at approximately r = 0.35–0.45, explaining about 12–20% of variance in academic performance.

Common mistakes to avoid

Concluding causation from correlation — the most common statistical error in popular media reporting.
Not checking for non-linear relationships — r = 0 does NOT mean no relationship, only no linear relationship (a U-shaped relationship has r ≈ 0).
Ignoring range restriction — correlation measured in a narrow subgroup will understate the true population correlation.

Correlation analysis is a descriptive and inferential statistical tool. Findings should not be used to establish causation without additional evidence such as controlled experiments or causal inference methods. For research publication, consult a statistician.