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The problems in this worksheet are taken from past exams. Work on
them on paper, since the exams you take in this course
will also be on paper.
We encourage you to complete this
worksheet in a live discussion section. Solutions will be made available
after all discussion sections have concluded. You don’t need to submit
your answers anywhere.
Note: We do not plan to cover all
problems here in the live discussion section; the problems we don’t
cover can be used for extra practice.
Researchers from the San Diego Zoo, located within Balboa Park, collected physical measurements of three species of penguins (Adelie, Chinstrap, or Gentoo) in a region of Antarctica. One piece of information they tracked for each of 330 penguins was its mass in grams. The average penguin mass is 4200 grams, and the standard deviation is 840 grams.
We’re interested in investigating the differences between the masses of Adelie penguins and Chinstrap penguins. Specifically, our null hypothesis is that their masses are drawn from the same population distribution, and any observed differences are due to chance only.
Below, we have a snippet of working code for this hypothesis test,
for a specific test statistic. Assume that adelie_chinstrap
is a DataFrame of only Adelie and Chinstrap penguins, with just two
columns – 'species' and 'mass'.
stats = np.array([])
num_reps = 500
for i in np.arange(num_reps):
# --- line (a) starts ---
shuffled = np.random.permutation(adelie_chinstrap.get('species'))
# --- line (a) ends ---
# --- line (b) starts ---
with_shuffled = adelie_chinstrap.assign(species=shuffled)
# --- line (b) ends ---
grouped = with_shuffled.groupby('species').mean()
# --- line (c) starts ---
stat = grouped.get('mass').iloc[0] - grouped.get('mass').iloc[1]
# --- line (c) ends ---
stats = np.append(stats, stat)Which of the following statements best describe the procedure above?
This is a standard hypothesis test, and our test statistic is the total variation distance between the distribution of Adelie masses and Chinstrap masses
This is a standard hypothesis test, and our test statistic is the difference between the expected proportion of Adelie penguins and the proportion of Adelie penguins in our resample
This is a permutation test, and our test statistic is the total variation distance between the distribution of Adelie masses and Chinstrap masses
This is a permutation test, and our test statistic is the difference in the mean Adelie mass and mean Chinstrap mass
Currently, line (c) (marked with a comment) uses .iloc. Which of the following options compute the exact same statistic as line (c) currently does?
Option 1:
stat = grouped.get('mass').loc['Adelie'] - grouped.get('mass').loc['Chinstrap']Option 2:
stat = grouped.get('mass').loc['Chinstrap'] - grouped.get('mass').loc['Adelie']Option 1 only
Option 2 only
Both options
Neither option
Is it possible to re-write line (c) in a way that uses
.iloc[0] twice, without any other uses of .loc
or .iloc?
Yes, it’s possible
No, it’s not possible
For your convenience, we copy the code for the hypothesis test below.
stats = np.array([])
num_reps = 500
for i in np.arange(num_reps):
# --- line (a) starts ---
shuffled = np.random.permutation(adelie_chinstrap.get('species'))
# --- line (a) ends ---
# --- line (b) starts ---
with_shuffled = adelie_chinstrap.assign(species=shuffled)
# --- line (b) ends ---
grouped = with_shuffled.groupby('species').mean()
# --- line (c) starts ---
stat = grouped.get('mass').iloc[0] - grouped.get('mass').iloc[1]
# --- line (c) ends ---
stats = np.append(stats, stat)What would happen if we removed line (a), and replaced
line (b) with
with_shuffled = adelie_chinstrap.sample(adelie_chinstrap.shape[0], replace=False)Select the best answer.
This would still run a valid hypothesis test
This would not run a valid hypothesis test, as all values in the
stats array would be exactly the same
This would not run a valid hypothesis test, even though there would
be several different values in the stats array
This would not run a valid hypothesis test, as it would incorporate information about Gentoo penguins
For your convenience, we copy the code for the hypothesis test below.
stats = np.array([])
num_reps = 500
for i in np.arange(num_reps):
# --- line (a) starts ---
shuffled = np.random.permutation(adelie_chinstrap.get('species'))
# --- line (a) ends ---
# --- line (b) starts ---
with_shuffled = adelie_chinstrap.assign(species=shuffled)
# --- line (b) ends ---
grouped = with_shuffled.groupby('species').mean()
# --- line (c) starts ---
stat = grouped.get('mass').iloc[0] - grouped.get('mass').iloc[1]
# --- line (c) ends ---
stats = np.append(stats, stat)What would happen if we removed line (a), and replaced
line (b) with
with_shuffled = adelie_chinstrap.sample(adelie_chinstrap.shape[0], replace=True)Select the best answer.
This would still run a valid hypothesis test
This would not run a valid hypothesis test, as all values in the
stats array would be exactly the same
This would not run a valid hypothesis test, even though there would
be several different values in the stats array
This would not run a valid hypothesis test, as it would incorporate information about Gentoo penguins
For your convenience, we copy the code for the hypothesis test below.
stats = np.array([])
num_reps = 500
for i in np.arange(num_reps):
# --- line (a) starts ---
shuffled = np.random.permutation(adelie_chinstrap.get('species'))
# --- line (a) ends ---
# --- line (b) starts ---
with_shuffled = adelie_chinstrap.assign(species=shuffled)
# --- line (b) ends ---
grouped = with_shuffled.groupby('species').mean()
# --- line (c) starts ---
stat = grouped.get('mass').iloc[0] - grouped.get('mass').iloc[1]
# --- line (c) ends ---
stats = np.append(stats, stat)What would happen if we replaced line (a) with
with_shuffled = adelie_chinstrap.assign(
species=np.random.permutation(adelie_chinstrap.get('species')
)and replaced line (b) with
with_shuffled = with_shuffled.assign(
mass=np.random.permutation(adelie_chinstrap.get('mass')
)Select the best answer.
This would still run a valid hypothesis test
This would not run a valid hypothesis test, as all values in the
stats array would be exactly the same
This would not run a valid hypothesis test, even though there would
be several different values in the stats array
This would not run a valid hypothesis test, as it would incorporate information about Gentoo penguins
Suppose we run the code for the hypothesis test and see the following empirical distribution for the test statistic. In red is the observed statistic.
Suppose our alternative hypothesis is that Chinstrap penguins weigh more on average than Adelie penguins. Which of the following is closest to the p-value for our hypothesis test?
0
\frac{1}{4}
\frac{1}{3}
\frac{2}{3}
\frac{3}{4}
1
For this question we will use data from the 2021 Women’s National Basketball Association (WNBA) season for the next several problems. In basketball, players score points by shooting the ball into a hoop. The team that scores the most points wins the game.
We have access to the season DataFrame, which contains
statistics on all players in the WNBA in the 2021 season. The first few
rows of season are shown below.
Each row in season corresponds to a single player. For
each player, we have: - 'Player' (str), their
name - 'Team' (str), the three-letter code of
the team they play on - 'G' (int), the number
of games they played in the 2021 season - 'PPG'
(float), the number of points they scored per game played -
'APG' (float), the number of assists (passes)
they made per game played - 'TPG' (float), the
number of turnovers they made per game played
Note that all of the numerical columns in season must
contain values that are greater than or equal to 0.
Suppose we only have access to the DataFrame
small_season, which is a random sample of size
36 from season. We’re interested in learning about
the true mean points per game of all players in season
given just the information in small_season.
To start, we want to bootstrap small_season 10,000 times
and compute the mean of the resample each time. We want to store these
10,000 bootstrapped means in the array boot_means.
Here is a broken implementation of this procedure.
boot_means = np.array([])
for i in np.arange(10000):
resample = small_season.sample(season.shape[0], replace=False) # Line 1
resample_mean = small_season.get('PPG').mean() # Line 2
np.append(boot_means, new_mean) # Line 3For each of the 3 lines of code above (marked by comments), specify what is incorrect about the line by selecting one or more of the corresponding options below. Or, select “Line _ is correct as-is” if you believe there’s nothing that needs to be changed about the line in order for the above code to run properly.
What is incorrect about Line 1? Select all that apply.
Currently the procedure samples from small_season, when
it should be sampling from season
The sample size is season.shape[0], when it should be
small_season.shape[0]
Sampling is currently being done without replacement, when it should be done with replacement
Line 1 is correct as-is
What is incorrect about Line 2? Select all that apply.
Currently it is taking the mean of the 'PPG' column in
small_season, when it should be taking the mean of the
'PPG' column in season
Currently it is taking the mean of the 'PPG' column in
small_season, when it should be taking the mean of the
'PPG' column in resample
.mean() is not a valid Series method, and should be
replaced with a call to the function np.mean
Line 2 is correct as-is
What is incorrect about Line 3? Select all that apply.
The result of calling np.append is not being reassigned
to boot_means, so boot_means will be an empty
array after running this procedure
The indentation level of the line is incorrect –
np.append should be outside of the for-loop
(and aligned with for i)
new_mean is not a defined variable name, and should be
replaced with resample_mean
Line 3 is correct as-is
IKEA is a Swedish furniture company that designs and sells ready-to-assemble furniture and other home furnishings.
An IKEA fan created an app where people can log the amount of time it
took them to assemble their IKEA furniture. The DataFrame
app_data has a row for each product build that was logged
on the app. The columns are:
'product' (str): the name of the product,
which includes the product line as the first word, followed by a
description of the product'category' (str): a categorical
description of the type of product'assembly_time' (str): the amount of time
to assemble the product, formatted as 'x hr, y min' where
x and y represent integers, possibly zeroThe first few rows of app_data are shown below, though
app_data has many more rows than pictured (5000 rows
total).
Assume that we have already run import babypandas as bpd
and import numpy as np.
We want to use app_data to estimate the average amount
of time it takes to build an IKEA bed (any product in the ‘bed’
category). Which of the following strategies would be an appropriate way
to estimate this quantity? Select all that apply.
Query to keep only the beds. Then resample with replacement many
times. For each resample, take the mean of the 'minutes'
column. Compute a 95% confidence interval based on those means.
Query to keep only the beds. Group by 'product' using
the mean aggregation function. Then resample with replacement many
times. For each resample, take the mean of the 'minutes'
column. Compute a 95% confidence interval based on those means.
Resample with replacement many times. For each resample, first query
to keep only the beds and then take the mean of the
'minutes' column. Compute a 95% confidence interval based
on those means.
Resample with replacement many times. For each resample, first query
to keep only the beds. Then group by 'product' using the
mean aggregation function, and finally take the mean of the
'minutes' column. Compute a 95% confidence interval based
on those means.
True or False: Suppose that from a sample, you compute a 95% bootstrapped confidence interval for a population parameter to be the interval [L, R]. Then the average of L and R is the mean of the original sample.
Suppose Tiffany has a random sample of dogs. Select the most appropriate technique to answer each of the following questions using Tiffany’s dog sample.
Do small dogs typically live longer than medium and large dogs?
Standard hypothesis test
Permutation test
Bootstrapping
Does Tiffany’s sample have an even distribution of dog kinds?
Standard hypothesis test
Permutation test
Bootstrapping
What’s the median weight for herding dogs?
Standard hypothesis test
Permutation test
Bootstrapping
Do dogs live longer than 12 years on average?
Standard hypothesis test
Permutation test
Bootstrapping