correlation
Sign in to savethumb|400px|right|Several sets of (x, y) points, with the Pearson correlation coefficient of x and y for each set. The correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case, the correlation coefficient is undefined because the variance of Y is zero.
AI overview
Correlation measures how closely two variables move together in a linear fashion, telling you both the strength and direction of their relationship. It matters because it helps you quickly identify whether two things tend to rise and fall together or move in opposite directions, though it won't tell you how steep that relationship is or whether the connection is actually caused by one variable affecting the other.
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Article
20 sectionsContents
- Coefficients
- Pearson's product-moment coefficient
- Correlation and independence
- Sample correlation coefficient
- Example
- Rank correlation coefficients
- Common misconceptions
- Correlation and causality
- Simple linear correlations
- Properties
- Uncorrelatedness and independence of stochastic processes
- Sensitivity to the data distribution
- Correlation matrices
- Nearest valid correlation matrix
- Bivariate normal distribution
- Other measures of association among random variables{{anchor|Other measures of dependence among random variables}}
- See also
- References
- Further reading
- External links
thumb|400px|right|Several sets of (x, y) points, with the Pearson correlation coefficient of x and y for each set. The correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case, the correlation coefficient is undefined because the variance of Y is zero.
In statistics, correlation is a kind of statistical relationship between two random variables or bivariate data. Usually it refers to the degree to which a pair of variables are linearly related. In statistics, more general relationships between variables are called an association, the degree to which some of the variability of one variable can be accounted for by the other.