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How to calculate the correlation coefficient between two random variables?
To learn how to calculate the covariance between any two random variables X and Y. To learn a shortcut, or alternative, formula for the covariance between two random variables X and Y. To learn a formal definition of the correlation coefficient between two random variables X and Y.
How to interpret the correlation coefficient between X and Y?
To learn how to interpret the correlation coefficient between any two random variables X and Y. To learn that if X and Y are independent random variables, then the covariance and correlation between X and Y are both zero. To learn that if the correlation between X and Y is 0, then X and Y are not necessarily independent.
When to use the correlation coefficient in business?
If the random variables are not highly correlated, then the manager would know that it would be okay to have one of the items available without the other. As the title of the lesson suggests, the correlation coefficient is the statistical measure that is going to allow us to quantify the degree of correlation between two random variables X and Y.
What kind of reality is the correlation coefficient?
Whatever the framework within which we order phenomena, however, that reality we perceive is of dependence, concomitance, covariation, coincidence, concurrence; or of independence, disassociation, or disconnectedness.
If the random variables are highly correlated, then the manager would know to make sure that both are available on a given day. If the random variables are not highly correlated, then the manager would know that it would be okay to have one of the items available without the other.
How to quantify the relationship between two random variables?
In this lesson, we’ll extend our investigation of the relationship between two random variables by learning how to quantify the extent or degree to which two random variables X and Y are associated or correlated.
How is the Pearson correlation coefficient related to the distribution?
The Pearson correlation coefficient doesn’t require the variables to have a certain distribution. The only connection I see between the case you are describing and correlation is that, if the variables are uncorrelated, it does not mean they are independent (which is simply the general case).