Understanding confidence intervals
In this exercise, you'll develop your intuition for how various parameter values impact confidence intervals. Specifically, you will explore through the get_ci()
function how changes widen or tighten the confidence interval. This is the function signature, where cl
is the confidence level and sd
is the standard deviation.
def get_ci(value, cl, sd):
loc = sci.norm.ppf(1 - cl/2)
rng_val = sci.norm.cdf(loc - value/sd)
lwr_bnd = value - rng_val
upr_bnd = value + rng_val
return_val = (lwr_bnd, upr_bnd)
return(return_val)
This exercise is part of the course
Customer Analytics and A/B Testing in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute and print the confidence interval
confidence_interval = get_ci(____, ____, ____)
print(confidence_interval)