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sciPy function stats.sem () | python

scipy.stats.sem (arr, axis = 0, ddof = 0) 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 :

# stats.sem () method

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 (< / code> "arr1:" , arr1)

print ( "arr2:" , arr2)

 

print ( "sem ratio for arr1:"

stats.sem (arr1, axis = 0 , ddof = 0 ))

 

print ( "sem ratio for arr1:"

stats.sem (arr1, axis = 1 , ddof = 0 ))

 

print ( "sem ratio for arr1:"

stats.sem (arr2, axis = 0 , ddof = 0 )) 

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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