Sampling with replacement

Bootstrapping is great for calculating confidence intervals for means; you'll now practice doing just that!

nba_weights contains the weights of a group of NBA players in kilograms:

nba_weights = [96.7, 101.1, 97.9, 98.1, 98.1, 
               100.3, 101.0, 98.0, 97.4]

You are interested in calculating the 95% confidence interval of the mean weight of NBA players using this list.

The following have been imported for you: random, and numpy as np.

This exercise is part of the course

Monte Carlo Simulations in Python

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Exercise instructions

Use random.choices() to sample nine heights from the list 1,000 times, with replacement.
Calculate the mean and 95% confidence interval for your simulation results, assigning the lower end of the confidence interval to lower and the upper end to upper.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

simu_weights = []

# Sample nine values from nba_weights with replacement 1000 times
for i in range(____):
    bootstrap_sample = ____
    simu_weights.append(np.mean(bootstrap_sample))

# Calculate the mean and 95% confidence interval of the mean for your results
mean_weight = ____
upper = ____
lower = ____
print(mean_weight, lower, upper)

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