Can someone please check the question and my code below and let me know why I am not getting the expected results?
Question: The table shows the contingency table of marital status by education. Use Chi-Square test for testing Homogenity.contingency table of marital status by education.
View the table by executing the following command python
from prettytable import PrettyTable
t = PrettyTable([‘Marital Status’,’Middle school’, ‘High School’,’Bachelor’,’Masters’,’PhD’])
t.add_row([‘Single’,18,36,21,9,6])
t.add_row([‘Married’,12,36,45,36,21])
t.add_row([‘Divorced’,6,9,9,3,3])
t.add_row([‘Widowed’,3,9,9,6,3])
print (t)
exit()
Hypothesis
Null Hypothesis: There is no difference in distribution between the types of education level in terms of marital status.
Alternate Hypothesis: There is a Difference
Coding
1. import chi2_contingency
and from scipy.stats import chi2
.
2.Declare a 2D array with the values mentioned in the contingency table of marital status by education.
3.Calculate and print the values of
– Chi-Square Statistic – Degree of Freedom – P value – Hint: Use chi2_contigency()
function 4.Assume the alpha value to be 0.05
5.Compare the P value with alpha and decide whether or not to reject the null hypothesis.
– If Rejected print “Reject the Null Hypothesis” – Else print “Failed to reject the Null Hypothesis”
Sample output 2.33 4.5 8.9 Reject the Null Hypothesis
My Code:
from scipy.stats import chi2_contingency
from scipy.stats import chi2
table= [ [18,36,21,9,6],[12,36,45,36,21], [6,9,9,3,3],[3,9,9,6,3] ]
stat,p,dof,expected = chi2_contingency(table)
prob = 0.95
critical = chi2.ppf(prob, dof)
if abs(stat) >= critical:
print(stat, dof ,p ,'Reject the Null Hypothesis')
else:
print(stat, dof ,p ,'Failed to reject the Null Hypothesis')
Thank You, Rakesh
use prob = 0.05 instead of 0.95
Thanks!
from scipy.stats import chi2_contingency from scipy.stats import chi2
def chi_test(): # Notation Output 1. stat: Float 2. dof: Integer 3. p_val: Float 4. res: String
table=[[18,36,21,9,6],[12,36,45,36,21],[6,9,9,3,3],[3,9,9,6,3]]
stat,dof,p_val,res=chi2_contingency(table)
prob=0.95
critical=chi2.ppf(prob, dof)
if abs(stat) >= critical:
print('Reject the Null Hypothesis')
else:
print('Failed to reject the Null Hypothesis')
alpha=1.0-prob
if p_val <= alpha:
print('Reject the Null Hypothesis')
else:
print('Failed to reject the Null Hypothesis')
return stat,dof,p_val,res
if name ==' main ': print(chi_test())
from scipy.stats import chi2_contingency
from scipy.stats import chi2
def chi_test():
'''
Output
1. stat: Float
2. dof : Integer
3. p_val: Float
4. res: String
'''
#Note: Round off the Float values to 2 decimal places.
table=[[18,36,21,9,6],[12,36,45,36,21],[6,9,9,3,3],[3,9,9,6,3]]
stat,p_val,dof,res=chi2_contingency(table)
prob=0.95
critical=chi2.ppf(prob, dof)
if abs(stat) >= critical:
res = 'Reject the Null Hypothesis'
else:
res= 'Failed to reject the Null Hypothesis'
alpha=1.0-prob
if p_val <= alpha:
res = 'Reject the Null Hypothesis'
else:
res = 'Failed to reject the Null Hypothesis'
stat = round(stat,2)
dof = round(dof,2)
p_val = round(p_val,2)
return stat,dof,p_val,res
if __name__=='__main__':
print(chi_test())
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