Correlation and covariance measure a. The strength of a linear relationship between two numerical variables b. The direction of a linear relationship between two numerical variables c. The strength and direction of a linear relationship between two numerical variables d. The strength and direction of a linear relationship between two categorical variables

c) The strength and direction of a linear relationship between two numerical variables

Step-by-step explanation:

Correlation coefficient measures the relationship strength between two variables.The range of Correlation is from -1 to +1. If it has a range of +1 there is a perfect positive linearity while -1 means there is a perfect negative linearity

Covariance coefficient measures the linear relationship and direction between two variables.

Both covariance and correlation measures the linear relationship between two variables.

Correlation and covariance are similar but they have many differences. Correlation is better than covarianve when measuring the relationship between two variables, because it is not affected by the change in scale and location.

step-by-step explanation:

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step-by-step explanation: