Question 5 (3.75 points) Which of the following is the correct option for text v

Question

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)

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

b)

'smaller', 'larger', 'between'

c)

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

d)

'left', 'right', 'between'

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Question 6 (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 6 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 = 'smaller'
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 = 'larger'
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 = 'not-equal'
means_1samp_ttest(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 = 'smaller'
prop_1samp_ztest(mean, std_dev, n, null_value, alternative)

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Question 7 (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 7 options:

a)

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)

b)

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)

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 = 'smaller'
prop_1samp_ztest(x, n, null_value, alternative)

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Question 8 (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 8 options:

a)

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)

b)

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)

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)

a)

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

b)

'smaller', 'larger', 'between'

c)

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

d)

'left', 'right', 'between'

Explanation / Answer

Solution:

5

a)

is the correct option.

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

6

a) is the correct option for min average test.

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)?

7)

b) is corect option here.

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)?

8)

a) is correct option.

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)

I hope this helps if you find any problem. Please comment below. Don't forget to give a thumbs up if you liked it. :)

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