Correlation Calculator
Pearson Correlation (r)
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R-Squared
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Strength
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Sample Size (n)
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Understanding Pearson Correlation
The Pearson correlation coefficient (r) measures the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, where +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.
R-squared (the coefficient of determination) is the square of the correlation coefficient and represents the proportion of variance in one variable that is explained by the other. An R-squared of 0.81 means that 81% of the variation in Y can be explained by X.
Important: correlation does not imply causation. Two variables can be strongly correlated due to a common underlying factor, reverse causation, or pure coincidence. Always consider the context and potential confounding variables when interpreting correlation results. Also, Pearson correlation only measures linear relationships — two variables can have a strong nonlinear relationship with a low Pearson r.
Frequently Asked Questions
What is a strong correlation?
An absolute r value above 0.7 is generally considered strong, 0.5-0.7 is moderate, 0.3-0.5 is weak, and below 0.3 is very weak. However, what constitutes strong depends on the field of study.
Does correlation prove causation?
No. Correlation shows that two variables move together but does not prove one causes the other. Ice cream sales and drowning deaths are correlated, but both are caused by hot weather, not each other.
How many data points do I need?
A minimum of 10-15 data points is recommended for a meaningful correlation analysis. With fewer points, the correlation can be heavily influenced by outliers and may not be statistically reliable.
What is the difference between Pearson and Spearman correlation?
Pearson measures linear relationships between continuous variables. Spearman measures monotonic relationships (consistently increasing or decreasing) and works with ordinal data. Use Spearman when data is not normally distributed or contains outliers.