Plotting distributions
Understanding how both discrete (e.g. binomial) and continuous (e.g. normal) probability distributions play a role in A/B testing enables us to gain additional visual insights into the nature of the data and deeper conceptual understanding of the theories that power the statistical frameworks of online experimentation.
Of great importance are two distributions: the binomial and the normal distributions. Follow the instructions to create plots and explore their parameters.
Questo esercizio fa parte del corso
A/B Testing in Python
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
from scipy.stats import binom
# Plot a binomial distribution
p = ____
n = ____
x = np.arange(n*p - 100, n*p + 100)
binom_a = ____.____(____, ____, ____)
plt.bar(x, binom_a)
plt.xlabel('Purchased')
plt.ylabel('PMF')
plt.show()