sciPy stats.sem() function | Python (original) (raw)

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:

Python3 1== `

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 ("\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))

`

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