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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

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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 and lift_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()
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