Test for single proportions
In Chapter 1, you calculated a p-value for a test hypothesizing that the proportion of late shipments was greater than 6%. In that chapter, you used a bootstrap distribution to estimate the standard error of the statistic. An alternative is to use an equation for the standard error based on the sample proportion, hypothesized proportion, and sample size.
\(z = \dfrac{\hat{p} - p_{0}}{\sqrt{\dfrac{p_{0}*(1-p_{0})}{n}}}\)
You'll revisit the p-value using this simpler calculation.
late_shipments is available. pandas and numpy are available under their usual aliases, and norm is loaded from scipy.stats.
This exercise is part of the course
Hypothesis Testing in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Hypothesize that the proportion of late shipments is 6%
p_0 = ____
# Calculate the sample proportion of late shipments
p_hat = ____
# Calculate the sample size
n = ____
# Print p_hat and n
print(p_hat, n)