sem(a, axis=0, ddof=1, nan_policy='propagate')
Calculate the standard error of the mean (or standard error of measurement) of the values in the input array.
The default value for ddof is different to the default (0) used by other ddof containing routines, such as np.std and np.nanstd.
An array containing the values for which the standard error is returned.
Axis along which to operate. Default is 0. If None, compute over the whole array a.
Delta degrees-of-freedom. How many degrees of freedom to adjust for bias in limited samples relative to the population estimate of variance. Defaults to 1.
Defines how to handle when input contains nan. The following options are available (default is 'propagate'):
- 'propagate': returns nan
- 'raise': throws an error
- 'omit': performs the calculations ignoring nan values
The standard error of the mean in the sample(s), along the input axis.
Compute standard error of the mean.
import numpy as np
from scipy import stats
a = np.arange(20).reshape(5,4)
stats.sem(a)
stats.sem(a, axis=None, ddof=0)
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.stats._mstats_basic:sem
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them