Plotting the difference distribution
Now lets plot the difference distribution of our results that is, the distribution of our lift.
The cont_var
and test_var
as well as the cont_conv
and test_conv
have been loaded for you. Additionally the upper and lower confidence interval bounds of this distribution have been provided as lwr_ci
and upr_ci
respectively.
This exercise is part of the course
Customer Analytics and A/B Testing in Python
Exercise instructions
- Calculate mean of the lift distribution by subtracting the control conversion rate (
cont_conv
) from the test conversion rate (test_conv
) - Generate the range of x-values for the difference distribution, making it 3 standard deviations wide.
- Plot a normal distribution by specifying the calculated
lift_mean
andlift_sd
. - Plot a green vertical line at the distributions mean, and a red vertical lines at each of the lower and upper confidence interval bounds. This has been done for you, so hit 'Submit Answer' to see the result!
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Find the lift mean and standard deviation
lift_mean = ____
lift_sd = (test_var + cont_var) ** 0.5
# Generate the range of x-values
lift_line = np.linspace(lift_mean - 3 * _____, lift_mean + 3 * _____, 100)
# Plot the lift distribution
plt.plot(lift_line, norm.pdf(lift_line, _____, _____))
# Add the annotation lines
plt.axvline(x = lift_mean, color = 'green')
plt.axvline(x = lwr_ci, color = 'red')
plt.axvline(x = upr_ci, color = 'red')
plt.show()