In [1]:

    

import warnings
warnings.filterwarnings('ignore')


    

In [3]:

    

%matplotlib inline
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [4]:

    

# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html


    

In [14]:

    

from scipy.stats import chisquare


    

In [15]:

    

observations = [16, 18, 16, 14, 12, 12]


    

In [16]:

    

chisquare(observations)


    

    
Out[16]:





Power_divergenceResult(statistic=2.0, pvalue=0.84914503608460956)

In [ ]:

    

= [16, 16, 16, 16, 16, 8]


    

In [7]:

    

chisquare(observations, f_exp=)


    

    
Out[7]:





Power_divergenceResult(statistic=3.5, pvalue=0.62338762774958223)

In [8]:

    

obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T


    

In [9]:

    

obs


    

    
Out[9]:





array([[16, 32],
       [18, 24],
       [16, 16],
       [14, 28],
       [12, 20],
       [12, 24]])

In [10]:

    

chisquare(obs)


    

    
Out[10]:





Power_divergenceResult(statistic=array([ 2.        ,  6.66666667]), pvalue=array([ 0.84914504,  0.24663415]))

In [11]:

    

chisquare(obs, axis=None)


    

    
Out[11]:





Power_divergenceResult(statistic=23.31034482758621, pvalue=0.015975692534127565)

In [12]:

    

obs.ravel()


    

    
Out[12]:





array([16, 32, 18, 24, 16, 16, 14, 28, 12, 20, 12, 24])

In [13]:

    

chisquare([16, 18, 16, 14, 12, 12], ddof=1)
(2.0, 0.73575888234288467)


    

    
Out[13]:





(2.0, 0.7357588823428847)

In [18]:

    

# http://vassarstats.net/textbook/ch8pt1.html

chisquare([89, 120, 91])


    

    
Out[18]:





Power_divergenceResult(statistic=6.0199999999999996, pvalue=0.049291678760462164)

In [19]:

    

chisquare([89, 120, 91], [100, 100, 100])


    

    
Out[19]:





Power_divergenceResult(statistic=6.0199999999999996, pvalue=0.049291678760462164)

In [ ]: