Question 1 (3.75 points) Suppose it is known that 20% of registered voters favor

Question

Question 1 (3.75 points)

Suppose it is known that 20% of registered voters favor banning public smoking. A sample of 1,200 is selected, and 348 voters are found to be in favor of banning public smoking. Which of the following Python lines are used to perform the hypothesis test that more than 20% of registered voters support banning public smoking?

Question 1 options:

a)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'larger'
prop_1samp_ztest(x, n, null_value, alternative)

b)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'smaller'
prop_1samp_ztest(x, n, null_value, alternative)

c)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'not-equal'
prop_1samp_ztest(x, n, null_value, alternative)

d)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'larger'
means_1samp_ztest(x, n, null_value, alternative)

Question 2 (3.75 points)

Which of the following Python functions is used to perform a hypothesis test for a population proportion?

Question 2 options:

a)

prop_1samp_ztest(x, n, null_value, alternative)

b)

prop_hypothesis_test(x, n, null_value, alternative)

c)

prop_1samp_hypothesistest(x, n, null_value, alternative)

d)

means_1samp_ttest(mean, std_dev, n, null_value, alternative)

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Question 3 (3.75 points)

From previously held examinations in a class, it is known that the average score of a midterm examination is 85. Suppose a sample is now collected of this midterm examination for a class and saved in the ExamScores.csv file. Which of the following Python lines can be used to perform a hypothesis test to conclude whether the average score of the exam is less than 85?

Question 3 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()
null_value = 85
alternative = 'not-equal'
means_1samp_ttest(mean, std_dev, n, null_value, alternative)

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()
null_value = 85
alternative = 'smaller'
means_1samp_ttest(mean, std_dev, n, null_value, alternative)

c)

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()
null_value = 85
alternative = 'smaller'
prop_1samp_ztest(mean, std_dev, n, null_value, alternative)

d)

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()
null_value = 85
alternative = 'larger'
means_1samp_ttest(mean, std_dev, n, null_value, alternative)

Question 4 (3.75 points)

From previously held exams, it is known that 18% of the class scores above 90 in a midterm examination. Suppose a sample is now collected of this midterm examination for a class and saved in the ExamScores.csv file. Which of the following Python lines can be used to perform a hypothesis test to conclude if less than 18% of the class scored more than 90?

Question 4 options:

a)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 0.90).values.sum()
null_value = 0.18
alternative = 'smaller'
prop_1samp_ztest(x, n, null_value, alternative)

b)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
null_value = 0.18
alternative = 'smaller'
prop_1samp_ztest(x, n, null_value, alternative)

c)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
null_value = 0.18
alternative = 'smaller'
means_1samp_ztest(x, n, null_value, alternative)

d)

import pandas as pd
scores = pd.read_csv('ExamScores.csv')
n = scores[['Exam1']].count()
x = (scores[['Exam1']] > 90).values.sum()
null_value = 0.18
alternative = 'larger'
prop_1samp_ztest(x, n, null_value, alternative)

Question 5 (3.75 points)

Which of the following is the correct option for text values that the alternative hypothesis can take in Python functions for hypothesis tests for population proportions?

Question 5 options:

a)

'left', 'right', 'between'

b)

'smaller', 'larger', 'between'

c)

'left', 'right', 'not-equal'

d)

'smaller', 'larger', 'not-equal'

Question 6 (3.75 points)

A simple random sample of size n = 50 is obtained from a population that is known to be normally distributed with mean 25. The sample mean is found to be 28.5 and the sample standard deviation is 5.1. Which of the following Python lines can be used to perform a hypothesis test to conclude whether the population mean is not equal to 25?

Question 6 options:

a)

n = 50
df = n - 1
mean = 28.5
stdev = 5.1
null_value = 25
alternative = 'not-equal'
print(means_1samp_ttest(mean, std_dev, n, null_value, alternative))

b)

n = 50
df = n - 1
mean = 25
stdev = 5.1
null_value = 28.5
alternative = 'not-equal'
print(means_1samp_ttest(mean, std_dev, n, null_value, alternative))

c)

n = 50
df = n - 1
mean = 28.5
stdev = 5.1
null_value = 25
alternative = 'smaller'
print(means_1samp_ttest(mean, std_dev, n, null_value, alternative))

d)

n = 50
df = n - 1
mean = 28.5
stdev = 5.1
null_value = 25
alternative = 'larger'
print(means_1samp_ttest(mean, std_dev, n, null_value, alternative))

Question 7 (3.75 points)

Which of the following Python functions is used to perform a hypothesis test for a population mean?

Question 7 options:

a)

means_1samp_ttest(mean, std_dev, n, null_value, alternative)

b)

prop_hypothesis_test(x, n, null_value, alternative)

c)

prop_1samp_hypothesistest(x, n, null_value, alternative)

d)

prop_1samp_ztest(x, n, null_value, alternative)

Question 8 (3.75 points)

Which of the following is the correct option for text values that the alternative hypothesis can take in Python functions for hypothesis tests for population means?

Question 8 options:

a)

'left', 'right', 'not-equal'

b)

'smaller', 'larger', 'not-equal'

c)

'left', 'right', 'between'

d)

'smaller', 'larger', 'between'

a)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'larger'
prop_1samp_ztest(x, n, null_value, alternative)

b)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'smaller'
prop_1samp_ztest(x, n, null_value, alternative)

c)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'not-equal'
prop_1samp_ztest(x, n, null_value, alternative)

d)

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'larger'
means_1samp_ztest(x, n, null_value, alternative)

Explanation / Answer

1.option 'a' is correct.

from snhu_MAT243 import prop_1samp_ztest
n = 1200
x = 348
null_value = 0.20
alternative = 'larger'
prop_1samp_ztest(x, n, null_value, alternative)

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