Estimation of mean improvement
You will now estimate how big this current effect is. Compute the mean fractional improvement for being in a high-numbered lane versus a low-numbered lane, along with a 95% confidence interval of the mean.
This exercise is part of the course
Case Studies in Statistical Thinking
Exercise instructions
- Compute the mean fractional difference using
np.mean()
. The variablef
from the last exercise is already in your namespace. - Draw 10,000 bootstrap replicates of the mean fractional difference using
dcst.draw_bs_reps()
. Store the result in anumpy
array namedbs_reps
. - Compute the 95% confidence interval using
np.percentile()
. - Hit 'Submit Answer' to print the mean fractional improvement and 95% confidence interval to the screen.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the mean difference: f_mean
f_mean = ____
# Draw 10,000 bootstrap replicates: bs_reps
bs_reps = ____
# Compute 95% confidence interval: conf_int
conf_int = ____
# Print the result
print("""
mean frac. diff.: {0:.5f}
95% conf int of mean frac. diff.: [{1:.5f}, {2:.5f}]""".format(f_mean, *conf_int))