Assertion: distribution's skewness | /r/HasProperty | positive
Facets:
Source sentences:
text: More generally one can raise the data to a power p in order to fit symmetrical distributions to data obeying a distribution of any skewness, whereby p < 1 when the skewness is positive and p > 1 when the skewness is negative.
text: More generally one can raise the data to a power p in order to fit symmetrical distributions to data obeying a distribution of any skewness, whereby p < 1 when the skewness is positive and p > 1 when the skewness is negative.
text: The accompanying plot of skewness as a function of variance and mean shows that maximum variance (1/4) is coupled with zero skewness and the symmetry condition (μ = 1/2), and that maximum skewness (positive or negative infinity) occurs when the mean is located at one end or the other, so that the "mass" of the probability distribution is concentrated at the ends (minimum variance).
text: The resulting distribution looks similar to the binomial, with the skewness being positive but decreasing with l. Figure
text: Altering these parameters can yield distributions that exhibit either positive or negative skewness, as shown in figure 6A.12:
text: Altering these parameters can yield distributions that exhibit either positive or negative skewness, as shown in figure 6A.12: