🔗 Correlation Calculator
Enter two equal-length lists of paired numbers to get the Pearson correlation coefficient, R², and a strength label — a quick read on how tightly your variables move together.
🔗 Relate Two Variables
What is a Correlation Calculator?
It measures how two variables move in relation to each other. Feed it paired observations — hours studied and exam score, temperature and sensor drift, ad spend and sign-ups — and it returns Pearson's r, the coefficient of determination R², and a plain-language label so you can judge both the direction and the strength of the linear link at a glance.
Use it in exploratory analysis to find which variables track together before you build a model, or to sanity-check a relationship you expect. Keep in mind that Pearson's r only captures straight-line association and that correlation never proves causation — a strong r is an invitation to investigate, not a conclusion.
❓ Frequently Asked Questions
What does the Pearson correlation coefficient measure?
Pearson's r measures the strength and direction of the linear relationship between two variables. It ranges from −1 to +1: +1 is a perfect positive line, −1 a perfect inverse line, and 0 no linear relationship. It only captures straight-line association — two variables can be strongly related in a curved way yet have an r near zero.
What is R² (the coefficient of determination)?
R² is simply r squared, and it represents the proportion of the variation in one variable that is explained by its linear relationship with the other. An r of 0.7746 gives an R² of about 0.6, meaning roughly 60% of the variation is accounted for by the linear fit.
How are the strength labels defined?
This tool labels the absolute value of r as Weak below 0.3, Moderate from 0.3 up to 0.7, and Strong at 0.7 or above. These are common rules of thumb — the meaningful threshold varies by field, so treat the label as a guide rather than a verdict.
Does a strong correlation prove causation?
No. A high correlation shows two variables move together, but it does not establish that one causes the other. A hidden third factor may drive both, or the link may be coincidental. Use correlation to spot relationships worth investigating, then design experiments to test causation.