The covariance is not used as much as the correlation because
A)it is not always a valid predictor of linear relationships.
B)it is difficult to calculate.
C)it is difficult to interpret because it depends on the units of measurement.
D)of all of these options.

Question

Quizplus · Accepted Answer

Covariance can indeed be difficult to interpret because its value is influenced by the scale of the variables involved, making comparisons across different datasets or variables challenging without standardization, which is why correlation, a standardized form of covariance, is often preferred for assessing the strength and direction of linear relationships.

The Covariance Is Not Used as Much as the Correlation