Calculating a z-score
Since variables have arbitrary ranges and units, we need to standardize them. For example, a hypothesis test that gave different answers if the variables were in Euros instead of US dollars would be of little value. Standardization avoids that.
One standardized value of interest in a hypothesis test is called a z-score. To calculate it, you need three numbers: the sample statistic (point estimate), the hypothesized statistic, and the standard error of the statistic (estimated from the bootstrap distribution).
The sample statistic is available as late_prop_samp.
late_shipments_boot_distn is a bootstrap distribution of the proportion of late shipments, available as a list.
pandas and numpy are loaded with their usual aliases.
This exercise is part of the course
Hypothesis Testing in Python
Exercise instructions
- Hypothesize that the proportion of late shipments is 6%.
- Calculate the standard error from the standard deviation of the bootstrap distribution.
- Calculate the z-score.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Hypothesize that the proportion is 6%
late_prop_hyp = ____
# Calculate the standard error
std_error = ____
# Find z-score of late_prop_samp
z_score = ____
# Print z_score
print(z_score)