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:
# 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
