Tagged: non-negative random variable

How to Prove Markov’s Inequality and Chebyshev’s Inequality

Problem 759

(a) Let $X$ be a random variable that takes only non-negative values. Prove that for any $a > 0$,
\[P(X \geq a) \leq \frac{E[X]}{a}.\] This inequality is called Markov’s inequality.

(b) Let $X$ be a random variable with finite mean $\mu$ and variance $\sigma^2$. Prove that for any $a >0$,
\[P\left(|X – \mu| \geq a \right) \leq \frac{\sigma^2}{a^2}.\] This inequality is called Chebyshev’s inequality.

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