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# Random sampling with numpy.random.choice()

Bootstrapping is a procedure similar to jackknifing. The basic bootstrapping method has the following steps:

1. Generate samples from the original data of size N. Visualize the original data sample as a bowl of numbers. We create new samples by taking numbers at random from the bowl. After taking a number, we return it to the bowl.
2. For each generated sample, we compute the statistical estimator of interest (for example, the arithmetic mean).

## How to do it...

We will apply `numpy.random.choice()` to do bootstrapping:

1. Generate a data sample following the binomial distribution that simulates flipping a fair coin five times:
```N = 400
np.random.seed(28)
data = np.random.binomial(5, .5, size=N)```
2. Generate 30 samples and compute ...

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