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Covariance matrix and covariance are not the same, although they are related: 

• Covariance: 

  Covariance measures the degree to which two variables change together. It indicates the direction of the linear relationship between two variables. If the covariance is positive, it means that as one variable increases, the other tends to increase as well. If the covariance is negative, it means that as one variable increases, the other tends to decrease. However, the magnitude of covariance doesn't tell us about the strength of the relationship. 

• Covariance Matrix: 

  A covariance matrix is a square matrix that summarizes the covariance between multiple variables. In a covariance matrix, the diagonal elements represent the variance of each variable, and the off-diagonal elements represent the covariance between each pair of variables. Covariance matrices are commonly used in multivariate statistics and linear algebra for various analyses, such as principal component analysis (PCA) and multivariate normal distributions. 

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