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:

Covariance and correlation measure linear association between two variables, for example X and Y.

Covariance:

Population parameter describes linear association between X and Y for the population while the sample statistic or estimator is used with simple data to estimate the linear association between X and Y for the population.

Correlation:

Correlation measures the degree of linear association between two variables, say X and Y. There are no units - dividing covariance by the standard deviations eliminates units. Correlation is a pure number. The range is from - 1 to + 1. If correlation coefficient is - 1, it means perfect negative linear association, + 1 means perfect positive linear association.

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.