Question 1 (3.75 points) Consider the following Python commands: import scipy.st

Question

Question 1 (3.75 points)

Consider the following Python commands:

import scipy.stats as st
st.norm.interval(0.99, 0.50, 0.05),

What does the 0.99 represent?

Question 1 options:

a)

level of significance

b)

proportion

c)

standard error

d)

mean

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Question 2 (3.75 points)

If n = 100 and mean = 219 and sample standard deviation = 35, which of the following Python lines outputs the 95% confidence interval?

Question 2 options:

a)

n = 100
df = n - 1
mean = 219
stderror = 0.5/(n**35)
print(st.t.interval(0.95, df, mean, stderror))

b)

n = 219
df = n - 1
mean = 100
stderror = 35.0/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

c)

n = 100
df = n - 1
mean = 219
stderror = 35.0/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

d)

n = 100
df = n - 1
mean = 219
stderror = 35.0/(n**0.5)
print(st.t.interval(0.90, df, mean, stderror))

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Question 3 (3.75 points)

Which of the following Python lines calculates the 99% confidence interval for proportion of Exam1 scores with scores greater than 90 from a CSV file named ExamScores?

Question 3 options:

a)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
p = x/n*1.0
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.90, p, stderror))

b)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
p = x/n*1.0
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

c)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
x = scores[['Exam1']].count()
n = (scores[['Exam1']] > 90).values.sum()
p = x/n*1.0
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

d)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] >= 90).values.sum()
p = x/n*1.0
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

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Question 4 (3.75 points)

Which of the following Python functions is used to calculate a confidence interval based on Student’s t-distribution?

Note: st is from the import command import scipy.stats as st

Question 4 options:

a)

st.t.normal

b)

st.t.interval

c)

st.t.confidence_interval

d)

st.norm.confidence_interval

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Question 5 (3.75 points)

If n = 99 and proportion (p) = 0.75, which of the following Python lines outputs the 99% confidence interval?

Question 5 options:

a)

import scipy.stats as st
n = 99
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.95, p, stderror))

b)

import scipy.stats as st
n = 99
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

c)

import scipy.stats as st
n = 99
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.98, p, stderror))

d)

import scipy.stats as st
n = 99
p = 0.25
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

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Question 6 (3.75 points)

If n = 99 and proportion (p) = 0.75, which of the following Python lines outputs the 95% confidence interval?

Question 6 options:

a)

import scipy.stats as st
n = 99
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.95, p, stderror))

b)

import scipy.stats as st
n = 100
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.95, p, stderror))

c)

import scipy.stats as st
n = 99
p = 0.25
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.95, p, stderror))

d)

import scipy.stats as st
n = 99
p = 0.75
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

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Question 7 (3.75 points)

Which of the following Python functions is used to calculate a confidence interval based on normal distribution?

Note: st is from the import command import scipy.stats as st

Question 7 options:

a)

st.t.confidence_interval

b)

st.norm.confidence_interval

c)

st.norm.normal

d)

st.norm.interval

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Question 8 (3.75 points)

Which of the following Python lines calculates the 95% confidence interval for the mean of variable Exam1 from a CSV file named ExamScores?

Question 8 options:

a)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
df = n - 1
mean = scores[['Exam1']].mean()
stdev = scores[['Exam1']].std()
stderror = stdev/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

b)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
df = n - 1
mean = scores[['Exam1']].mean()
stdev = scores[['Exam1']].std()
stderror = stdev/(n**0.5)
print(st.t.interval(0.99, df, mean, stderror))

c)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
df = n - 1
mean = scores[['Exam1']].std()
stdev = scores[['Exam1']].mean()
stderror = stdev/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

d)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
mean = scores[['Exam1']].mean()
stdev = scores[['Exam1']].std()
stderror = stdev/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

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a)

level of significance

b)

proportion

c)

standard error

d)

mean

Explanation / Answer

1)
ANS: a) level of significance

2) c.
n = 100
df = n - 1
mean = 219
stderror = 35.0/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

3) d
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
p = x/n*1.0
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

4)
b) st.t.interval

5) d.
import scipy.stats as st
n = 99
p = 0.25
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.99, p, stderror))

6) c.
import scipy.stats as st
n = 99
p = 0.25
stderror = (p * (1 - p)/n)**0.5
print(st.norm.interval(0.95, p, stderror))

7) d.
d)st.norm.interval

8) a.

mport pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
df = n - 1
mean = scores[['Exam1']].mean()
stdev = scores[['Exam1']].std()
stderror = stdev/(n**0.5)
print(st.t.interval(0.95, df, mean, stderror))

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