Introduction:
The sign of the correlation refers to whether the relationship between two variables is positive or negative. It indicates the direction of the relationship. In this case, the correct answer is option B, which states that the sign of the correlation is determined by the covariance between X and Y.

Explanation:
Covariance:
Covariance measures the relationship between two variables. It indicates how changes in one variable are related to changes in another variable. In the context of correlation, covariance is calculated by taking the average of the products of the deviations of X and Y from their respective means.

Sign of Covariance:
The sign of the covariance between X and Y determines the sign of the correlation. If the covariance is positive, it means that when X increases, Y also tends to increase. In this case, the correlation is positive. On the other hand, if the covariance is negative, it means that when X increases, Y tends to decrease. In this case, the correlation is negative.

Standard Deviation:
The standard deviation is a measure of the dispersion or variability of a dataset. It quantifies the average amount by which each data point in a dataset differs from the mean. While the standard deviation is an important measure for understanding the spread of the data, it does not determine the sign of the correlation.

X and Y Variables:
The X and Y variables themselves do not determine the sign of the correlation. They represent the two variables being analyzed, and their relationship is measured through the correlation coefficient. The correlation coefficient quantifies the strength and direction of the relationship between X and Y.

Conclusion:
In summary, the sign of the correlation is determined by the covariance between X and Y. A positive covariance indicates a positive correlation, while a negative covariance indicates a negative correlation. The standard deviation and the X and Y variables do not determine the sign of the correlation.