Correlation Coefficient Calculator

Your details

Enter your first variable. Each value pairs with the value at the same position in the Y list.
Enter your second variable. It must have the same number of values as X.
Pearson r measures linear association. Spearman rho ranks the data first and is more robust to outliers. Kendall tau-b is best for small samples with many tied ranks.
The threshold p-value below which the correlation is deemed statistically significant. The 95% level (alpha = 0.05) is the standard in most fields.
Display the least-squares best-fit line equation y = mx + b alongside the correlation coefficient.
Correlation coefficientStrong positive correlation
0.8528
Strengthstrong positive correlation
r² (coefficient of determination)0.7273
Pairs (n)5
t-statistic2.8284
Degrees of freedom3
p-value (two-tailed)0.1892
Significant?No (p >= 0.05)
95% CI lower bound-0.119
95% CI upper bound0.9901
Covariance (population)1.6
Regression slope (m)0.8
Regression intercept (b)1.8
Regression equationy = 0.8x + (1.8)
0.8528
Very strong -<-0.9Strong --0.9--0.7Moderate --0.7--0.4Weak --0.4--0.1None-0.1-0.1Weak +0.1-0.4Moderate +0.4-0.7Strong +0.7-0.9Very strong +0.9+

r = 0.8528, a strong positive correlation.

  • The sign of the coefficient shows direction: as x rises, y tends to rise. The magnitude (0 to 1) shows how tightly the points track a straight line.
  • r² = 0.7273, meaning about 72.7% of the variation in y is statistically associated with x via this linear fit.
  • The p-value is 0.1892: No (p >= 0.05). A small p-value means the observed correlation is unlikely under the null hypothesis of no association.
  • Correlation is not causation. A high coefficient can arise from a confounding variable, reversed causality, or coincidence. Establish causation through controlled experiments or careful study design.

Next stepUse the regression equation y = 0.8x + (1.8) to predict y for any x. Plot a scatter chart to confirm the relationship looks linear.

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