sciPy stats.sem() function | Python
Last Updated :
18 Feb, 2019
scipy.stats.sem(arr, axis=0, ddof=0) function is used to compute the standard error of the mean of the input data.
Parameters :
arr : [array_like]Input array or object having the elements to calculate the standard error.
axis : Axis along which the mean is to be computed. By default axis = 0.
ddof : Degree of freedom correction for Standard Deviation.
Results : standard error of the mean of the input data.
Example:
import numpy as np
from scipy import stats
arr1 = [[ 20 , 2 , 7 , 1 , 34 ],
[ 50 , 12 , 12 , 34 , 4 ]]
arr2 = [ 50 , 12 , 12 , 34 , 4 ]
print ( "\narr1 : " , arr1)
print ( "\narr2 : " , arr2)
print ( "\nsem ratio for arr1 : " ,
stats.sem(arr1, axis = 0 , ddof = 0 ))
print ( "\nsem ratio for arr1 : " ,
stats.sem(arr1, axis = 1 , ddof = 0 ))
print ( "\nsem ratio for arr1 : " ,
stats.sem(arr2, axis = 0 , ddof = 0 ))
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Output :
arr1 : [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]]
arr2 : [50, 12, 12, 34, 4]
sem ratio for arr1 : [10.60660172 3.53553391 1.76776695 11.66726189 10.60660172]
sem ratio for arr1 : [5.62423328 7.61892381]
sem ratio for arr1 : 7.618923808517841
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