Correlation Coefficient (r) Calculator
Analyze the relationship between two sets of data by calculating the Pearson correlation coefficient (r).
Correlation Coefficient (r): -
Interpretation: -
Use this correlation coefficient calculator, correlation calculator, coefficient calculator, data correlation tool, variable relationship calculator calculator for quick, clear estimates. Try a tiny example to see the impact of each input.
Q: What does a correlation coefficient tell you?
A correlation coefficient, typically denoted as ‘r’, measures the strength and direction of a linear relationship between two quantitative variables. Its value ranges from -1 to +1. A value near +1 indicates a strong positive linear relationship, a value near -1 indicates a strong negative linear relationship, and a value near 0 indicates a weak or no linear relationship.
What is a good correlation coefficient?
A “good” correlation coefficient depends on the field of study and the specific context, but generally, the closer the absolute value of ‘r’ is to 1, the stronger the linear relationship. Coefficients like ±0.7 to ±1.0 are often considered strong, ±0.3 to ±0.69 moderate, and below ±0.3 weak. It’s important to remember that correlation does not imply causation.
How do you interpret a correlation coefficient of 0.8?
A correlation coefficient of 0.8 indicates a strong positive linear relationship between two variables. This means that as one variable increases, the other variable tends to increase as well, and this relationship is quite consistent. For example, if ‘r’ between study hours and test scores is 0.8, it suggests that more study hours are strongly associated with higher test scores.
What is the difference between positive and negative correlation?
Positive correlation means that two variables tend to move in the same direction: as one variable increases, the other also tends to increase, and as one decreases, the other tends to decrease. Negative correlation, conversely, means that two variables tend to move in opposite directions: as one variable increases, the other tends to decrease.