Linear Dependence between Two Bernoulli Random Variables

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The Pearson correlation coefficient, denoted , is a measure of the linear dependence between two random variables, that is, the extent to which a random variable can be written as , for some and some . This Demonstration explores the following question: what correlation coefficients are possible for a random vector , where is a Bernoulli random variable with parameter and is a Bernoulli random variable with parameter ? Interestingly, a two-dimensional Bernoulli random vector has a correlation coefficient that is constrained by the choices of and .

Contributed by: Jeff Hamrick (August 2008)
Open content licensed under CC BY-NC-SA



For the sake of simplicity, you are constrained to choose and that are slightly bounded away from 0 and 1.

In the upper left-hand corner, there is a possible joint distribution of that accommodates your choices of , , and . Additionally, at the bottom is shown (with a red line) the linear correlation coefficients that are attainable for fixed choices of and . The basic lesson is clear: it is not possible to couple together two arbitrary Bernoulli random variables in such a way that any conceivable linear correlation coefficient is attained. Notice that choosing maximizes the range of possible values of .

For a two-dimensional random vector , where both and are discrete random variables, finding a joint probability distribution that triggers a particular linear correlation coefficient is equivalent to solving a system of linear equations. It is not surprising, then, that sometimes this system of linear equations has no solution, a unique solution, or infinitely many solutions.

If in the -dimensional random vector the are Gaussian, then finding a joint probability distribution with a particular variance-covariance matrix (and, therefore, particular pairwise linear correlation coefficients between the coordinates of the -dimensional random vector) also happens to be equivalent to solving a system of linear equations. This fact is true for any elliptical distribution as well, that is, a distribution with a density function whose constant curves are ellipsoids. In general, however, the dependence between two or more random variables cannot be captured by a single number.

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