Sample data is collected in ExamScores.csv that includes scores in the first and

Question

Sample data is collected in ExamScores.csv that includes scores in the first and second exams for students in a class. The variables are called Exam1 and Exam2 respectively. The professor is interested in finding out whether the average score in the second exam is different from the average score in the first exam, treating the data as matched-pair. Which of the following Python lines can be used to perform this test?

Question options:

a)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
null_value = 0
alternative = 'not-equal'
print(st.ttest_rel(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative))

b)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
null_value = 0
alternative = 'not-equal'
print(st.ttest_ind(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative))

c)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
print(st.ttest_rel(exam1_paired_score, exam2_paired_score))

d)

from scipy.stats import ttest_ind_from_stats as ttest
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
print(ttest(exam1_paired_score, exam2_paired_score))

a)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
null_value = 0
alternative = 'not-equal'
print(st.ttest_rel(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative))

b)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
null_value = 0
alternative = 'not-equal'
print(st.ttest_ind(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative))

c)

import scipy.stats as st
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
print(st.ttest_rel(exam1_paired_score, exam2_paired_score))

d)

from scipy.stats import ttest_ind_from_stats as ttest
import pandas as pd
scores = pd.read_csv('ExamScores.csv')
exam1_paired_score = scores[['Exam1']]
exam2_paired_score = scores[['Exam2']]
print(ttest(exam1_paired_score, exam2_paired_score))

Explanation / Answer

Option c) is correct

Explanation:

The t test for matched pair is performed by the code

st.ttest_rel(exam1_paired_score, exam2_paired_score)

Option a) is incorrect

Explanation: There is an error in code

st.ttest_rel(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative)

Here the arguments are incorrect for matched pair t test, the correct argument is

st.ttest_rel(exam1_paired_score, exam2_paired_score)

Option b) is incorrect

Explanation:

Here the code

print(st.ttest_ind(exam1_paired_score, exam2_paired_score, equal_var=False, null_value, alternative)

returns the independent samples t-test result

Option d) is incorrect

Explanation:

Here the code

from scipy.stats import ttest_ind_from_stats as ttest

importing the t-test for independent samples t test.

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