Publication date: 08/13/2020

Parameterizations for Distributions

This section gives the density functions f for the distributions used in the Process Capability platform. It also gives expected values and variances for all but the Johnson and SHASH distributions.

Normal

, , ,

E(X) = μ

Var(X) = σ2

Beta

, , ,

E(X) =

Var(X) =

where B(·) is the Beta function.

Exponential

, ,

E(X) = σ

Var(X) = σ2

Gamma

, , ,

E(X) = ασ

Var(X) = ασ2

where Γ(·) is the gamma function.

Johnson

Johnson Su

, , ,

Johnson Sb

, ,

Johnson Sl

, ,

where φ(·) is the standard normal probability density function.

Lognormal

, , ,

E(X) =

Var(X) =

Mixture of Normals

The Mixture of 2 Normals and Mixture of 3 Normals options for Distribution share the following parameterization:

E(X) =

Var(X) =

where μi, σi, and πi are the respective mean, standard deviation, and proportion for the ith group, and φ(·) is the standard normal probability density function. For the Mixture of 2 Normals, k is equal to 2. For the Mixture of 3 Normals distribution, k is equal to 3. A separate mean, standard deviation, and proportion of the whole is estimated for each group in the mixture.

SHASH

, , 0 < δ, 0 < σ

where

φ(·) is the standard normal pdf

Note: When γ = 0 and δ = 1, the SHASH distribution is equivalent to the normal distribution with location θ and scale σ.

Weibull

, ,

E(X) =

Var(X) =

where Γ(·) is the gamma function.

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