2.854 | Fall 2016 | Graduate

# Introduction to Manufacturing Systems

Lecture Notes

## Notes on Covariance

$X,Y\,independent \rightarrow cov(X,Y) = 0$

Assume random variables $$X$$ and $$Y$$ are discrete. That is, assume that there is a finite or denumerable sample space which is a set of $$w_i$$ and a set of quantities $$x_i$$ and $$y_i$$ defined.

Definition $$X$$ and $$Y$$ are independent if

$prob((X = x)\,and\,(Y = y)) = prob(X = x)prob(Y = y)$

in which $$x$$ is some $$x_i$$ and $$y$$ is some $$y_j$$.

Then if $$X$$ and $$Y$$ are independent,

$E(XY) = E(X)E(Y)$

Proof:

$E(XY) = \sum _{i,j} \ x_iy_jprob(XY = x_iy_j)$

$= \sum _{i,j} \ x_iy_jprob((X = x_i)\,and\,(Y = y_j))$

$= \sum _{i,j} \ x_iy_jprob(X = x_i)prob(Y = y_j)$

$= \sum _{i} \ x_iprob(X = x_i)\sum _{j} \ y_jprob(Y = y_j) = E(X)E(Y)$

Then if $$X$$ and $$Y$$ are independent,

$cov(X,Y) = E[(X - E(X))(Y - E(Y))]$

$=E[XY - XE(Y) - YE(X)+E(X)E(Y)]$

$=E[XY] - E(X)E(Y) - E(Y)E(X) + E(X)E(Y) = 0$

Fall 2016
Lecture Notes