Correlation is scaled to be between -1 and +1 depending on whether there is positive or negative correlation, and is dimensionless. The covariance however, ranges from zero, in the case of two independent variables, to Var(X), in the case where the two sets of data are equal. The units of COV(X,Y) are the units of X times the units of Y. correlation is the expected value of two random variables (E[XY]),whereas covariance is expected value of variations of two random variable from their expected values,
The sum of two random variables that are normally distributed will be also be normally distributed. Use the link and check out the article. It'll save a cut and paste.
the length would be anywhere between 1 and 1/2 and so the expected value is 1/4
Variance" is a mesaure of the dispersion of the probability distribution of a random variable. Consider two random variables with the same mean (same aver-age value). If one of them has a distribution with greater variance, then, roughly speaking, the probability that the variable will take on a value far from the mean is greater.
Yes it is. That is actually true for all random vars, assuming the covariance of the two random vars is zero (they are uncorrelated).
we compute it by using their differences
For a population the mean and the expected value are just two names for the same thing. For a sample the mean is the same as the average and no expected value exists.
Two random samples are dependent if each data value in one sample can be paired with a corresponding data value in the other sample.
if two variables are positively related,it means that the two variables change in the same direction.that is,if the value of one of the variables increases,the value of the other variable too will increase.for example,if employment as an economic variable increases in a country,and price of goods too increases then we can say that these two variables(employment and price) are positively related
Yes, and the new distribution has a mean equal to the sum of the means of the two distribution and a variance equal to the sum of the variances of the two distributions. The proof of this is found in Probability and Statistics by DeGroot, Third Edition, page 275.
Infinitely many. Assuming the variables are "x" and "y" , for every value of "x" a value for "y" can be calculated.
If an equation has two variables, we'll call them (x,y), the variables can be any value as long as both sides of the equation have the same result. If the equation was x = y, then the variables could be (1,1), (2,2), (3,3),etc...