# Tag Archives: Linear Property

## Variance and Expected Value Algebra

Linearity of Expected Value: Suppose $X$ and $Y$ are random variables and $a$ and $b$ are scalars.  The following relationships hold: $E[aX+b] = aE[X]+b$ $E[aX+bY] = aE[X] +bE[Y]$

Variance: $Var(aX+bY) = a^2Var(X)+2abCov(X,Y)+b^2Var(Y)$

Suppose $X_i$ for $i=\left\{1\ldots n\right\}$ are $n$ independent identically distributed (iid) random variables.  Then $Cov(X_i,X_j) = 0$ for $i\ne j$ and $\displaystyle Var\left({\sum_{i=1}^n X_i}\right) = \sum_{i=1}^n Var(X_i)$

Example: $X$ is the stock price of AAPL at market close. $Y$ is the sum of closing AAPL stock prices for 5 days.  Then $\begin{array}{rl} Var(Y) &= \displaystyle \sum_{i=1}^5 Var(X_i) \\ &= 5Var(X) \end{array}$.

Contrast this with the variance of $Z = 5X$.  In other words, $Z$ is a random variable that takes a value of 5 times the price of AAPL at the close of any given day.  Then $\begin{array}{rl} Var(Z) &= Var(5X) \\ &=5^2Var(x) \end{array}$

The distinction between $Y$ and $Z$ is subtle but very important.

Variance of a Sample Mean:

In situations where the sample mean $\bar{X}$ is a random variable over $n$ iid observations (i.e. the average price of AAPL over 5 days), the following formula applies: $\begin{array}{rl} Var(\bar{X}) &= \displaystyle Var\left(\frac{1}{n} \displaystyle \sum_{i=1}^n X_i\right) \\ &= \displaystyle \frac{nVar(X)}{n^2} \\ &= \displaystyle \frac{Var(X)}{n} \end{array}$