Expectation and variance of a binary random variable

If you start dealing with Generalized linear models (GLMs) you will come across sentences like “Obviously the variance of the binary dependent variable is \mu(1-\mu).” Well, for everybody who does not find it too obvious the following derivation may help in understanding the mathematical reasoning behind GLMs, especially Logit and Probit models.

Assume a binary random variable X:

X= \begin{cases}0 \; \text{with probability} \; P(X=0) \\1 \; \text{with probability} \; P(X=1)\end{cases}

The relation P(X=0)=1-P(X=1) holds since the probabilities (of a discrete random variable) must sum to 1.

The Variance of a random variable is defined as

Var(X)=E \left \{[X-E(X)]^2 \right \}=E[X^2]-E[X]^2.

The expected value of our binary random variable is

E[X]=(1-P(X=1)) \cdot 0 + P(X=1) \cdot 1 = P(X=1).

E[X] therefore has the nice interpretation of being the probabilty of X taking on the value 1.

With that information we can derive the variance of a binary random variate:

\begin{array}{l l} Var(X) &= E[X^2]-E[X]^2 \\  &= E[X]-E[X]^2 \quad (*)\\  &=P(X=1)-P(X=1)^2 \\ &=P(X=1)(1-P(X=1)) \end{array}

(*) holds because X can only take on the values zero or one and it holds that 1^2=1 and 0^2=0.


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