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Normal probability plot of a sample from a right-skewed distribution – it has an inverted C shape. Histogram of a sample from a right-skewed distribution – it looks unimodal and skewed right. This is a sample of size 50 from a uniform distribution, plotted as both a histogram, and a normal probability plot.
The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include: mounded (or unimodal), U-shaped, J-shaped, reverse-J shaped and multi-modal. [1] A bimodal distribution would have two high points rather than one. The shape of a distribution is ...
The exponentially modified normal distribution is another 3-parameter distribution that is a generalization of the normal distribution to skewed cases. The skew normal still has a normal-like tail in the direction of the skew, with a shorter tail in the other direction; that is, its density is asymptotically proportional to for some positive .
In this manner, a distribution that is skewed to the right is transformed into a distribution that is skewed to the left and vice versa. Example . The F-expression of the positively skewed Gumbel distribution is: F=exp[-exp{-( X - u )/0.78 s }], where u is the mode (i.e. the value occurring most frequently) and s is the standard deviation .
Generalization of symmetrical distributions (like the normal and the logistic) makes them applicable to data obeying a distribution that is skewed to the right (using an exponent <1) as well as to data obeying a distribution that is skewed to the left (using an exponent >1). This enhances the versatility of symmetrical distributions.
The Laplace distribution; The Lévy skew alpha-stable distribution or stable distribution is a family of distributions often used to characterize financial data and critical behavior; the Cauchy distribution, Holtsmark distribution, Landau distribution, Lévy distribution and normal distribution are special cases. The Linnik distribution
λ = 0: distribution is exactly logistic; λ = 0.14: distribution is approximately normal; λ = 0.5: distribution is U-shaped; λ = 1: distribution is exactly uniform(−1, 1) If the Tukey lambda PPCC plot gives a maximum value near 0.14, one can reasonably conclude that the normal distribution is a good model for the data.
For a unimodal distribution (a distribution with a single peak), negative skew commonly indicates that the tail is on the left side of the distribution, and positive skew indicates that the tail is on the right. In cases where one tail is long but the other tail is fat, skewness does not obey a simple rule.