PPPLOT Statement: CAPABILITY Procedure

Summary of Theoretical Distributions

You can use the PPPLOT statement to request P-P plots based on the theoretical distributions summarized in the following table:

Table 5.56 Distributions and Parameters
			Parameters
Family	Distribution Function $\text{[math]}$	Range	Location	Scale	Shape
Beta	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$ , $\text{[math]}$
Exponential	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Gamma	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Gumbel	$\text{[math]}$	all $\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Inverse Gaussian	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$		$\text{[math]}$
	$\text{[math]}$
Lognormal	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Normal	$\text{[math]}$	all $\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Generalized Pareto	$\text{[math]}$	all $\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Power Function	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Rayleigh	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$
Weibull	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$

You can request these distributions with the BETA, EXPONENTIAL, GAMMA, GUMBEL, IGAUSS, NORMAL, LOGNORMAL, PARETO, POWER, RAYLEIGH, and WEIBULL options, respectively. If you do not specify a distribution option, a normal P-P plot is created.

To create a P-P plot, you must provide all of the parameters for the theoretical distribution. If you do not specify parameters, then default values or estimates are substituted, as summarized by the following table:

Table 5.57 Defaults for Parameters
Family	Default Values	Estimated Values
Beta	$\text{[math]}$ , $\text{[math]}$	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$
Exponential	$\text{[math]}$	maximum likelihood estimate for $\text{[math]}$
Gamma	$\text{[math]}$	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$
Gumbel	None	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$
Inverse Gaussian	None	sample estimate for $\text{[math]}$ , maximum likelihood estimate for $\text{[math]}$
Lognormal	$\text{[math]}$	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$
Normal	None	sample estimates for $\text{[math]}$ and $\text{[math]}$
Generalized Pareto	$\text{[math]}$	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$
Power Function	$\text{[math]}$ , $\text{[math]}$	maximum likelihood estimate for $\text{[math]}$
Rayleigh	$\text{[math]}$	maximum likelihood estimate for $\text{[math]}$
Weibull	$\text{[math]}$	maximum likelihood estimates for $\text{[math]}$ and $\text{[math]}$