Python scipy chisquare returns different values than R chisquare

chi-squared, numpy, python, r, scipy

Solution

For this `chisq.test` call python equivalent is `chi2_contingency`:

This function computes the chi-square statistic and p-value for the hypothesis test of independence of the observed frequencies in the contingency table observed.

>>> arr = np.array([38,27,23,17,11,4,98,100,80,85,60,23]).reshape(2,-1)
>>> arr
array([[ 38,  27,  23,  17,  11,   4],
       [ 98, 100,  80,  85,  60,  23]])
>>> chi2, p, dof, expected = scipy.stats.chi2_contingency(arr)
>>> chi2, p, dof
(7.0762165124844367, 0.21503342516989818, 5)

Problem

I am trying to use `scipy.stats.chisquare`. I have built a toy example: ``` In [1]: import scipy.stats as sps In [2]: import numpy as np In [3]: sps.chisquare(np.array([38,27,23,17,11,4]), np.array([98, 100, 80, 85,60,23])) Out[11]: (240.74951271813072, 5.302429887719704e-50) ``` The same example in `R` returns: ``` > chisq.test(matrix(c(38,27,23,17,11,4,98,100,80,85,60,23), ncol=2)) Pearson's Chi-squared test data: matrix(c(38, 27, 23, 17, 11, 4, 98, 100, 80, 85, 60, 23), ncol = 2) X-squared = 7.0762, df = 5, p-value = 0.215 ``` What am I doing wrong? Thanks

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