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Correlation in Random Variables

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Correlation in Random VariablesLecture 11Spring 2002Correlation in Random VariablesSuppose that an experimentproduces two Random vari-ables, about the relationship be-tween them?One of the best ways to visu-alize the possible relationshipis to plot the (X, Y)pairthatis produced by several trials ofthe experiment. An exampleof correlated samples is shownat the rightLecture 111Joint Density FunctionThe joint behavior ofXandYis fully captured in the joint probabilitydistribution. For a continuous distributionE[XmYn]= xmynfXY(x, y)dxdyFor discrete distributionsE[XmYn]= x Sx y SyxmynP(x, y)Lecture 112Covariance FunctionThe covariance function is a number that measures the commonvariation is defined ascov(X, Y)=E[(X E[X])(Y E[Y])]=E[XY] E[X]E[Y]The covariance is determined by the difference inE[XY]andE[X]E[Y].

Autocorrelation Function The autocorrelation function is very similar to the covariance func-tion. It is defined as R(X,Y)=E[XY]=cov(X,Y)+E[X]E[Y] It retains the mean values in the calculation of the value. The random variables are orthogonal if R(X,Y)=0. Lecture 11 5

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