A correlation matrix is a table that shows the pairwise correlations between variables in a dataset. Each entry in the matrix represents the strength and direction of the linear relationship between two variables, typically measured by Pearson’s correlation coefficient. The diagonal values are always 1 because each variable is perfectly correlated with itself. Correlation matrices are widely used in multivariate analyses like factor analysis, cluster analysis, and portfolio construction.
For example, in a study of health data, a correlation matrix might show the relationships among blood pressure, cholesterol, and age. A strong positive correlation between age and blood pressure would appear as a value close to 1, while a negative correlation between exercise frequency and cholesterol might show up as a negative value.
« Back to Glossary Index