mvsdist#
- scipy.stats.mvsdist(data)[source]#
- ‘Frozen’ distributions for mean, variance, and standard deviation of data. - Parameters:
- dataarray_like
- Input array. Converted to 1-D using ravel. Requires 2 or more data-points. 
 
- Returns:
- mdist“frozen” distribution object
- Distribution object representing the mean of the data. 
- vdist“frozen” distribution object
- Distribution object representing the variance of the data. 
- sdist“frozen” distribution object
- Distribution object representing the standard deviation of the data. 
 
 - See also - Notes - The return values from - bayes_mvs(data)is equivalent to- tuple((x.mean(), x.interval(0.90)) for x in mvsdist(data)).- In other words, calling - <dist>.mean()and- <dist>.interval(0.90)on the three distribution objects returned from this function will give the same results that are returned from- bayes_mvs.- References - T.E. Oliphant, “A Bayesian perspective on estimating mean, variance, and standard-deviation from data”, https://scholarsarchive.byu.edu/facpub/278, 2006. - Examples - >>> from scipy import stats >>> data = [6, 9, 12, 7, 8, 8, 13] >>> mean, var, std = stats.mvsdist(data) - We now have frozen distribution objects “mean”, “var” and “std” that we can examine: - >>> mean.mean() 9.0 >>> mean.interval(0.95) (6.6120585482655692, 11.387941451734431) >>> mean.std() 1.1952286093343936