Get Professional Statistics Homework Help in Covariance Assignment. Tutorchrome statistics assignment tutors offer 24x7 Covariance Assignment Writing Help to university students across the USA, Canada, UK, Australia and UAE countries.
COVARIANCE
It can be defined as the measure of how much two random variable change together if the greater values of one variable mainly correspond with the greater values of the other variable and this also holds for the smaller values. This means the variables tend to show similar behavior then this is positive.
In the opposite case, when the greater values of one variable mainly correspond to the smaller values of the other, then the variables tend to show opposite behavior, so the covariance is negative. The sign depicts the tendency in the linear relationship between the variables and the magnitude of the covariance is not easy to interpret. The Pearson product- moment correlation coefficient which is the normalized version shows its magnitude which is the strength of the linear relation.
A distinction must be made between the covariance of two random variables which is a statistical population that can be seen as a property of the joint probability distribution. This serves as a statistical estimation value of the parameter.
Covariance Assignment Help
In the case of two joint distribution real number-valued, random variable x and y with finite second moment s defined as follows as
where E[x] is the expected value of x which is also known as the mean of x and by using the linearity property of expectations which can be as follows
Last equation is for Loss of significance when computed with floating point arithmetic and thus should be avoided in computer programs when the data has not been centered before.
For random vector and, then the matrix is equal to
Covariance Homework Help
where mT is the transpose of the vector and the (i,j)-th element of this matrix is equal to the covariance Cov(xi, yj) which is the i-th scalar component of x and the j-th scalar component of y.
Cov(y, x) is considered as the transpose of Cov(x, y) and for a vector of m jointly distributed random variables with finite second moments, its covariance matrix is defined as follows
Random variables whose covariance is zero are called uncorrelated and the unit of measurement is Cov(x, y) is x times y. By contrast, the correlation depends on this which is a dimensionless number and is the measure of linear dependence.
