Explanation:

Regression analysis is a statistical technique used to explore the relationship between two or more variables. In this case, we are looking at a regression equation where X is dependent on Y. The term "bxy" refers to the regression coefficient between X and Y, which is calculated by dividing the covariance between X and Y by the variance of Y.

Formula:

The formula for calculating the regression coefficient (bxy) is:

bxy = cov(X,Y) / var(Y)

Where cov(X,Y) is the covariance between X and Y, and var(Y) is the variance of Y.

Calculating bxy:

In the given regression equation, X is dependent on Y. Therefore, we can rewrite the equation as:

Y = (35/8 - X) * (5/2)

Now, we can calculate the covariance and variance as follows:

cov(X,Y) = cov(X, (35/8 - Y*(2/5)))
= -2/5 * var(X)
= -2/5 * (35/8)^2

var(Y) = var((35/8 - X) * (5/2))
= (5/2)^2 * var(X)
= (5/2)^2 * (1/8)

Therefore, the regression coefficient (bxy) can be calculated as:

bxy = cov(X,Y) / var(Y)
= (-2/5 * (35/8)^2) / ((5/2)^2 * (1/8))
= -2.5

Conclusion:

The regression coefficient (bxy) between X and Y in the given regression equation is -2.5. This indicates a negative relationship between X and Y. As Y increases, X decreases.

Bxy=-2/5