Generating many bootstrap replicates
The function bootstrap_replicate_1d()
from the video is available in your namespace. Now you'll write another function, draw_bs_reps(data, func, size=1)
, which generates many bootstrap replicates from the data set. This function will come in handy for you again and again as you compute confidence intervals and later when you do hypothesis tests.
For your reference, the bootstrap_replicate_1d()
function is provided below:
def bootstrap_replicate_1d(data, func):
"""Generate bootstrap replicate of 1D data."""
bs_sample = np.random.choice(data, len(data))
return func(bs_sample)
This exercise is part of the course
Statistical Thinking in Python (Part 2)
Exercise instructions
- Define a function with call signature
draw_bs_reps(data, func, size=1)
.- Using
np.empty()
, initialize an array calledbs_replicates
of sizesize
to hold all of the bootstrap replicates. - Write a
for
loop that ranges oversize
and computes a replicate usingbootstrap_replicate_1d()
. Refer to the exercise description above to see the function signature ofbootstrap_replicate_1d()
. Store the replicate in the appropriate index ofbs_replicates
. - Return the array of replicates
bs_replicates
. This has already been done for you.
- Using
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
def draw_bs_reps(data, func, size=1):
"""Draw bootstrap replicates."""
# Initialize array of replicates: bs_replicates
bs_replicates = ____
# Generate replicates
for i in ____:
bs_replicates[i] = ____
return bs_replicates