This gives you a large number of estimates and so you can e.g. You can then estimate your model on all of those bootstrapped datasets. The intuitive idea behind the bootstrap is this: if your original dataset was a random draw from the full population, then if you take subsample from the sample (with replacement), then that too represents a draw from the full population.
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