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.
Este ejercicio forma parte del curso
A/B Testing in Python
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
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()