Applying confidence intervals
In practice, you aren't going to hand-code confidence intervals. Let's utilize the statsmodels package to streamline this process and examine some more tendencies of interval estimates.
In this exercise, we've generated a binomial sample of the number of heads in 50 fair coin flips saved as the heads variable. You'll compute a few different confidence intervals for this sample, and then scale your work for 10 similar samples.
The proportion_confint() function has already been imported to help you compute confidence intervals.
This exercise is part of the course
Practicing Statistics Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute and print the 99% confidence interval
confidence_int = proportion_confint(heads, ____, ____)
print(____)