def mquantiles_cimj(data, prob=[0.25, 0.5, 0.75], alpha=0.05, axis=None):
 alpha = min(alpha, (1 - alpha))
 z = norm.ppf((1 - (alpha / 2.0)))
 xq = mstats.mquantiles(data, prob, alphap=0, betap=0, axis=axis)
 smj = mjci(data, prob, axis=axis)
 return ((xq - (z * smj)), (xq + (z * smj)))

def mquantiles_cimj ( data , prob = [ 0.25 , 0.5 , 0.75 ] , alpha = 0.05 , axis = None ) : alpha = min ( alpha , ( 1 - alpha ) ) z = norm . ppf ( ( 1 - ( alpha / 2.0 ) ) ) xq = mstats . mquantiles ( data , prob , alphap = 0 , betap = 0 , axis = axis ) smj = mjci ( data , prob , axis = axis ) return ( ( xq - ( z * smj ) ) , ( xq + ( z * smj ) ) )
computes the alpha confidence interval for the selected quantiles of the data .