Given independent and identically distributed observations $X_{1}, \ldots, X_{n}$ with finite mean $E\left(X_{1}\right)=\mu$ and variance $\operatorname{Var}\left(X_{1}\right)=\sigma^{2}$, explain the notion of a bootstrap sample $X_{1}^{b}, \ldots, X_{n}^{b}$, and discuss how you can use it to construct a confidence interval $C_{n}$ for $\mu$.

Suppose you can operate a random number generator that can simulate independent uniform random variables $U_{1}, \ldots, U_{n}$ on $[0,1]$. How can you use such a random number generator to simulate a bootstrap sample?

Suppose that $\left(F_{n}: n \in \mathbb{N}\right)$ and $F$ are cumulative probability distribution functions defined on the real line, that $F_{n}(t) \rightarrow F(t)$ as $n \rightarrow \infty$ for every $t \in \mathbb{R}$, and that $F$ is continuous on $\mathbb{R}$. Show that, as $n \rightarrow \infty$,

$$\sup _{t \in \mathbb{R}}\left|F_{n}(t)-F(t)\right| \rightarrow 0 .$$

State (without proof) the theorem about the consistency of the bootstrap of the mean, and use it to give an asymptotic justification of the confidence interval $C_{n}$. That is, prove that as $n \rightarrow \infty, P^{\mathbb{N}}\left(\mu \in C_{n}\right) \rightarrow 1-\alpha$ where $P^{\mathbb{N}}$ is the joint distribution of $X_{1}, X_{2}, \ldots$

[You may use standard facts of stochastic convergence and the Central Limit Theorem without proof.]