trimmed_mean_ci#
- scipy.stats.mstats.trimmed_mean_ci(data, limits=(0.2, 0.2), inclusive=(True, True), alpha=0.05, axis=None)[source]#
- Selected confidence interval of the trimmed mean along the given axis. - Parameters:
- dataarray_like
- Input data. 
- limits{None, tuple}, optional
- None or a two item tuple. Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. If - nis the number of unmasked data before trimming, then (- n * limits[0])th smallest data and (- n * limits[1])th largest data are masked. The total number of unmasked data after trimming is- n * (1. - sum(limits)). The value of one limit can be set to None to indicate an open interval.- Defaults to (0.2, 0.2). 
- inclusive(2,) tuple of boolean, optional
- If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). - Defaults to (True, True). 
- alphafloat, optional
- Confidence level of the intervals. - Defaults to 0.05. 
- axisint, optional
- Axis along which to cut. If None, uses a flattened version of data. - Defaults to None. 
 
- Returns:
- trimmed_mean_ci(2,) ndarray
- The lower and upper confidence intervals of the trimmed data.