April 16, 2025 · 8:26 am

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Binomial Expansion – deriving the formula for Variance

We have seen how the formula for mean (expected value) was derived, and now we are going to look at variance.

In general variance of a probability distribution is

(1) $\begin{equation*} Var(X)=E(X^2)-(E(X))^2\end{equation*}$

We are going to start by calculating $E(X^2)$

$\begin{equation*}E(X^2)=\sum_{x=0}^nx^2p(x)\end{equation}$

$\begin{equation*}E(X^2)=\sum_{x=0}^nx^2\begin{pmatrix}n\\x\end{pmatrix}p^x(1-p)^{n-x}\end{equation}$

$\begin{equation*}E(X^2)=\sum_{x=0}^n x^2\frac{n!}{(n-x)!x!}p^x(1-p)^{n-x}\end{equation}$

The $x^2$ cancels with the $x!$ to leave $x$ on the numerator and $(x-1)!$ on the denominator.

Also, when $x=0, x^2=0$ and we can start the sum at $x=1$

$\begin{equation*}E(X^2)=\sum_{x=1}^n x\frac{n!}{(n-x)!(x-1)}!p^x(1-p)^{n-x}\end{equation}$

Let $y=x-1$ and $m=n-1$ , when $x=n, y=n-1$ and hence $y=m$ and when $x=1, y=0$

Our equation is now

$\begin{equation*}E(X^2)=\sum_{y=0}^m (y+1)\frac{(m+1)!}{(m+1-(y+1))!y!}p^{y+1}(1-p)^{m+1-(y+1)}\end{equation}$

Simplify

$\begin{equation*}E(X^2)=\sum_{y=0}^m(y+1)\frac{(m+1)!}{(m-y)!y!}p^{y+1}(1-p)^{m-y}\end{equation}$

$\begin{equation*}E(X^2)=\sum_{y=0}^m(y+1)(m+1)\frac{m!}{(m-y)!y!}p\times p^y(1-p)^{m-y}\end{equation}$

$\begin{equation*}E(X^2)=\sum_{y=0}^m p(y+1)(m+1)\frac{m!}{(m-y)!y!} p^y(1-p)^{m-y}\end{equation}$

$\begin{equation*}E(X^2)=\sum_{y=0}^m p(y+1)(m+1)p(y)\end{equation}$

$\begin{equation*}E(X^2)=p(m+1)(\sum_{y=0}^myp(y)+\sum_{y=0}^mp(y))\end{equation}$

$\sum_{y=0}^mp(y)=1$ and $\sum_{y=0}^myp(y)=E(Y)$

$\begin{equation*}E(X^2)=p(m+1)(E(Y)+1)\end{equation}$

$E(Y)=mp$

$\begin{equation*}E(X^2)=p(m+1)(mp+1)\end{equation}$

$m+1=n$

$\begin{equation*}E(X^2)=pn((n-1)p+1)\end{equation}$

$\begin{equation*}E(X^2)=n^2p^2-np^2+np\end{equation}$

Now from equation $1$

$\begin{equation*}Var(X)=E(X^2))-(E(X))^2\end{equation}$

$\begin{equation*}=Var(X)=n^2p^2-np^2+np-n^2p^2\end{equation}$

$\begin{equation*}Var(X)=-np^2+np\end{equation}$

(2) $\begin{equation*}Var(X)=np(1-p)\end{equation*}$

and the standard deviation is

(3) $\begin{equation*}\sigma_X=\sqrt{np(1-p)}\end{equation*}$

Filed under Algebra, Binomial, Probability Distributions, Standard Deviation

Tagged as binomial distribution, standard deviation of a binomial distribution, variance of a binomial distribution

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