Starting with variance

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andyeisenberg
2026-01-01 05:00:56 +00:00
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<title>Variance</title>
<p>
Text of section.
The expected value is one of several statistics sometimes referred to as a "measure of central tendency".
These are statistics that try to capture the "typical" value of the random variable.
Our next statistic tries to measure how spread out a distribution is.
</p>
<definition xml:id="def-variance">
<statement>
<p>
Let <m>X</m> be a random variable.
The <term>variance</term> of <m>X</m> is:
<md>
<mrow>\Var(X) = \E\left[ \left(X - \E(X)\right)^2 \right].</mrow>
</md>
</p>
</statement>
</definition>
<p>
This formula tries to capture some measure of how spread out the distribution is.
One way we might hope to measure spread is to calculate how far away <m>X</m> is from its expected value on average.
Squaring the term <m>X - \E(X)</m> ensures that any difference between the value of the random variable and its expected value contributes positively.
("Distance away from" should be positive.) So, the formula in <xref ref="def-variance"/> is conceptually clear.
In practice, there's another way to structure the formula that makes for cleaner calculation:
</p>
<theorem xml:id="thm-variance-formula">
<statement>
<p>
Let <m>X</m> be a random variable.
Then:
<md>
<mrow>\Var(X) = \E\left(X^2\right) - \left(\E(X)\right)^2.</mrow>
</md>
</p>
</statement>
</theorem>
<proof>
<p>
<md>
<mrow> \Var(X) \amp = \E\left[ \left(X - \E(X)\right)^2 \right] </mrow>
<mrow> \amp = \E\left[ X^2 - 2 \E(X) X + \left(\E(X)\right)^2\right] </mrow>
<mrow> \amp = \E\left(X^2\right) - 2 \E(X) \E(X) + \left(\E(X)\right)^2</mrow>
<mrow> \amp = \E\left(X^2\right) - \left(\E(X)\right)^2. </mrow>
</md>
</p>
</proof>
<exercises xml:id="exercises-Variance">
<exercise>
<statement>