在使用Python的X平方测试中没有得到预期的结果

Question

请有人检查下面的问题和我的代码，让我知道为什么我没有得到预期的结果？

问题：表按教育显示婚姻状况的偶然性表。采用卡方检验法对教育程度的Homogenity.contingency婚姻状况表进行检验.

通过执行以下命令python查看表

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()

假设

零假设:不同类型的教育程度在婚姻状况上的分布没有差异。

另一种假设:有区别

编码

1.import chi2_contingency和from scipy.stats import chi2。

2.按教育程度宣布一个2D数组，其中包含婚姻状况应急表中提到的值。

3.计算和打印

-卡方统计-自由度-P值-提示:使用chi2_contigency()函数4.假定α值为0.0 5

5.将P值与α值进行比较，确定是否拒绝零假设。

-如果被拒绝打印“拒绝零假设”-否则打印“没有拒绝零假设”

样本输出2.33 4.5 8.9拒绝零假设

我的代码：

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')

谢谢你，Rakesh

Answer 1

使用prob = 0.05而不是0.95

谢谢!

Answer 2

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())

Answer 3

从scipy.stats导入chi2_contingency从scipy.stats导入chi2

def chi_test()：# Notation输出1.stat:Float2.dof:整型3. p_val: Float4.res: String

代码

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   

如果name=='main'：打印(chi_test())