A random variable [math]X[/math] has [math]\chi^2_n[/math] (chi-squared with [math]n[/math] degrees of freedom) if it has the same distribution as [math]Z_1^2+ \ldots +Z_n^2[/math], where [math]Z_1, \ldots, Z_n[/math] are i.i.d [math]\cN(0,1)[/math].

• Let [math]Z \sim \cN(0,1)[/math]. Show that the moment generating function of [math]Y=Z^2-1[/math] satisfies
  [[math]] \phi(s):=E\big[e^{sY}\big]=\left\{ \begin{array}{ll} \displaystyle\frac{e^{-s}}{\sqrt{1-2s}}& \text{if } s \lt 1/2\\ \infty & \text{otherwise} \end{array}\right. [[/math]]
• Show that for all [math]0 \lt s \lt 1/2[/math],
  [[math]] \phi(s)\le \exp\Big(\frac{s^2}{1-2s}\Big)\,. [[/math]]
• Conclude that
  [[math]] \p(Y \gt 2t+2\sqrt{t})\le e^{-t} [[/math]]
  [math]\texttt{[Hint: you can use the convexity inequality $\sqrt{1+u}\le 1+u/2$]}.[/math]
• Show that if [math]X \sim \chi^2_n[/math], then, with probability at least [math]1-\delta[/math], it holds
  [[math]] X \le n+ 2 \sqrt{n\log(1/\delta)}+ 2\log(1/\delta) \,. [[/math]]