Visualization of resampling results

Now you'll visualize the results of your simulation from the previous exercise! You'll continue working with nba_weights, which 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]

Here is your simulation code from the previous exercise:

simu_weights = []
for i in range(1000):
    bootstrap_sample = random.choices(nba_weights, k=9)
    simu_weights.append(np.mean(bootstrap_sample))
mean_weight = np.mean(simu_weights)
upper = np.quantile(simu_weights, 0.975)
lower = np.quantile(simu_weights, 0.025)
print(mean_weight, lower, upper)

The simu_weights list you generated in the last exercise is loaded for you. Similarly, mean_weight, lower and upper are already defined as the mean and 2.5% and 97.5% quantile values for your confidence interval.

The following packages have already been loaded for you: random, numpy as np, seaborn as sns, and matplotlib.pyplot as plt.

This exercise is part of the course

Monte Carlo Simulations in Python

View Course

Exercise instructions

Use sns.displot() to plot the distribution of the simulated weights.
Use plt.axvline() to plot two vertical lines for the 95% confidence interval (plot lower followed by upper) in red, and the mean in green.

Hands-on interactive exercise

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

# Plot the distribution of the simulated weights
____

# Plot vertical lines for the 95% confidence intervals and mean
plt.axvline(____, color="red")
plt.axvline(____, color="red")
plt.axvline(____, color="green")
plt.show()

Edit and Run Code