Explain why the plug-in estimator for a property of a parametric distribution is so-named.
Given a parametric distribution, estimates for its parameters, and a function for how the parameters are related to a particular property of the distribution, compute a plug-in estimate for that property.

Explain the two “levels” of the parametric bootstrap in terms of what has actually happened in the real world and what is being simulated in bootstrap world.
State the “recipe” for generating bootstrap estimates from a parametric distribution.
Given a parametric distribution and estimators for its parameters, use the parametric bootstrap to obtain bootstrap estimates of a property of that distribution.

Compare and contrast the parametric and nonparametric bootstraps.
Explain the two “levels” of the nonparametric bootstrap in terms of what has actually happened in the real world and what is being simulated in bootstrap world.
State the “recipe” for generating bootstrap estimates using the nonparametric bootstrap.
Explain how to sample from the ECDF of a sample using sample().
Given an estimator for a property of a distribution, use the nonparametric bootstrap to obtain bootstrap estimates of that property of the distribution.
Explain under what scenarios a parametric or nonparametric bootstrap is more appropriate.

Construct a coverage \(1 - \alpha\) bootstrap percentile confidence interval for a property of a distribution using either parametric or nonparametric bootstrap estimates of that property.