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10/31/2018
hw09
Question 5:
Do our predicted and empirical values match? Why is this the case?
Hint:
Are there any laws that we learned about in class that might help explain this?

13/25
3. Polling and the Normal Distribution
Question 1
Michelle is a statistical consultant, and she works for a group that supports Proposition 68 (which would mandate
labeling of all horizontal or vertical axes), called Yes on 68. They want to know how many Californians will vote
for the proposition.
Michelle polls a uniform random sample of all California voters, and she finds that 210 of the 400 sampled voters
will vote in favor of the proposition. Fill in the code below to form a table with 3 columns: the first two columns
should be identical to
sample
. The third column should be named
Proportion
and have the proportion of
total voters that chose each option.
In [26]:
sample
=
Table()
.
with_columns(
"Vote"
,
make_array(
"Yes"
,
"No"
),
"Count"
, make_array(
210
,
190
))
sample_size
= 400
sample_with_proportions
=
sample
.
with_column(
'Proportion'
, sample
.
column(
'Coun
t'
)
/
sample_size)
sample_with_proportions
Out[26]:
Vote
Count
Proportion
Yes
210
0.525
No
190
0.475

10/31/2018
hw09
14/25
In [27]:
_
=
ok
.
grade(
'q3_1'
)
_
=
ok
.
backup()
Question 2
She then wants to use 10,000 bootstrap resamples to compute a confidence interval for the proportion of all
California voters who will vote Yes. Fill in the next cell to simulate an empirical distribution of Yes proportions with
10,000 resamples. In other words, use bootstrap resampling to simulate 10,000 election outcomes, and populate
resample_yes_proportions
with the yes proportion of each bootstrap resample. Then, visualize
resample_yes_proportions
with a histogram. You should see a bell shaped curve centered near the
proportion of Yes in the original sample.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Running tests
---------------------------------------------------------------------
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hw09
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In [28]:
resample_yes_proportions
=
make_array()
for
i
in
np
.
arange(
10000
):
resample
=
proportions_from_distribution(sample_with_proportions,
"Proport
ion"
, sample_size)
resample_yes_proportions
=
np
.
append(resample_yes_proportions, resample
.
co
lumn(