 Dear student, this example is related to the previous example. So, in this example, we have just f of x, y key value different. This is the PDF of the multivariate normal distribution. And we want to find the mean vector A matrix, sigma square x, sigma square y and the correlation. So, we know that A which is equals to sigma inverse and sigma inverse which is equals to sigma inverse and A inverse which is equals to sigma. And this is the mean vector. This equation, now the coefficient of x square which is equals to 1, coefficient of y square which is equals to 1, coefficient of x which is equals to 4 and the coefficient of y which is equals to 6. Look at this. Now, this is the coefficient of x square which is equals to 1, coefficient of Y scale which is equals to 1 and the twice time of A1 to up XY, now the coefficient of XY which is equals to 0. Now A which is equals to this one. And we know the coefficient of X, the equation how it is determined, we have seen in the previous video, how the equation is determined. Now we have given the coefficient of X value which is equals to 4. Now put this in 4. The value of the coefficient of A, now minus 2. We have determined the value of mu which is equals to 1. So minus 2, we have determined the value of mu of Y which is equals to 0. So the mu of X which is equals to minus 2. So again the coefficient of Y, we know the value of the coefficient of Y, we have determined. So the coefficient of Y which is equals to minus 6, 2, mu of Y's value determined A2, 2's value 1 put, then similarly mu X's value we have determined and mu 1, 2 which is equals to 0. So 0 multiplied by any value which is equals to 0. So cancel out these minus terms, minus value so 2 take divided by 2 which is equals to 3, mu of Y which is equals to 3. So mean vector minus 2 or 3, its value we have determined. This is the mean vector of 2. Next to find the sigma square Y, sigma square X and the correlation. We proceed as this. So we know that the A inverse which is equals to this one and we have entered the coefficient of this value. I mean variance is sigma square you have 1, 0, 0, 1. Basically what you have we have values A, A and A. So the covariance, previous we have term, this is the covariance term, this is the covariance and diagonal we have variance. Covariance is 0 and variance is we have 1 and 1. Now the correlation which is equals to covariance over variance, variance square root. So correlation cancel we have 0. Basically correlation range, you know that correlation range we have minus 1, 2, 1. So here correlation shows that they are independent. We have no correlation between two variables.