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2016 Paper 3 Q12
D: 1700.0 B: 1516.0
Chebyshev's inequality Poisson distribution binomial distribution probability bounds inequality mean and variance summation

Let \(X\) be a random variable with mean \(\mu\) and standard deviation \(\sigma\). Chebyshev's inequality, which you may use without proof, is \[ \P\left(\vert X-\mu\vert > k\sigma\right) \le \frac 1 {k^2} \,, \] where \(k\) is any positive number.

  1. The probability of a biased coin landing heads up is \(0.2\). It is thrown \(100n\) times, where \(n\) is an integer greater than 1. Let \(\alpha \) be the probability that the coin lands heads up \(N\) times, where \(16n \le N \le 24n\). Use Chebyshev's inequality to show that \[ \alpha \ge 1-\frac 1n \,. \]
  2. Use Chebyshev's inequality to show that \[ 1+ n + \frac{n^2}{ 2!} + \cdots + \frac {n^{2n}}{(2n)!} \ge \left(1-\frac1n\right) \e^n \,. \]

Solution:

  1. Let \(N\) be the number of times the coin lands heads up, ie \(N \sim Binomial(100n, 0.2)\), then \(\mathbb{E}(N) = \mu = 20n, \mathrm{Var}(N) = \sigma^2 = 100n \cdot 0.2 \cdot 0.8 = 16n \Rightarrow \sigma = 4\sqrt{n}\). \begin{align*} && \mathbb{P}(|X - \mu| > k\sigma) &\leq \frac{1}{k^2} \\ \Rightarrow && 1 - \mathbb{P}(|X - \mu| \leq k\sigma) &\leq \frac1{k^2} \\ \Rightarrow && 1 - \mathbb{P}(|X - 20n| \leq \sqrt{n} \cdot 4\sqrt{n}) &\leq \frac1{{\sqrt{n}}^2} \\ \Rightarrow && 1 - \mathbb{P}(16n \leq N \leq 24n) &\leq \frac{1}{n} \\ \Rightarrow && 1 - \frac1n &\leq \alpha \end{align*}
  2. Suppose \(X \sim Pois(n)\), then \(\mathbb{E}(X) = n, \mathrm{Var}(X) = n\). Therefore \begin{align*} && \mathbb{P}(|X - \mu| > k\sigma) &\leq \frac{1}{k^2} \\ \Rightarrow && 1-\mathbb{P}(|X - n| \leq \sqrt{n} \cdot \sqrt{n}) &> \frac{1}{\sqrt{n}^2} \\ \Rightarrow && 1 - \sum_{i=0}^{2n} \mathbb{P}(X = i) & \leq \frac{1}{n} \\ \Rightarrow && \sum_{i=0}^{2n} e^{-n} \frac{n^i}{i!} \geq 1 - \frac{1}{n} \\ \Rightarrow && \sum_{i=0}^{2n} \frac{n^i}{i!} \geq \left ( 1 - \frac1n \right)e^n \end{align*}

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