Recall the covariance of two random variables X and Y is defined as Cov(X, Y) = E[(X − E[X])(Y − E[Y])]. For a multivariate random variable Z (i. e., each index of Z is a random variable), we define the covariance matrix Σ such that Σi j = Cov(Zi , Zj). Concisely, Σ = E[(Z − µ)(Z − µ) > ], where µ is the mean value of the random column vector Z. Prove that the covariance matrix is always positive semidefinite (PSD).

12m

step-by-step explanation:

square root means have of a number so have of 3 is 1.5 then multiply it by 8 and you get your answer!

3. (5x + 1) - (-10x + 6)

simplify the answer choice. combine like terms. remember to distribute the negative to the terms inside the second parenthesis. also remember that two negatives = one positive, and one of each sign = negative.

- (-10x + 6) = + 10x - 6

simplify. combine like terms:

5x + 1 + 10x - 6

5x + 10x + 1 - 6

15x - 5

∴ it is your answer

~

180°

step-by-step explanation:

sum of equilateral triangle=180°

< n=60°

< m=120°°[angle supplementary of equilateral angle]

< m+< r+< x=360°(sum of exterior angle of triangle)