This section describes the probability distributions available in the RELIABILITY procedure for probability plotting and parameter estimation.
Probability plots can be constructed for each of the probability distributions in Table 16.57. For all distributions other than the threeparameter Weibull, estimates of two distribution parameters (location and scale or scale and shape) are computed by maximum likelihood or by least squares fitted to points on the probability plot. If one of the parameters is specified as fixed, the other is estimated. In addition, you can specify a fixed threshold, or shift, parameter for those distributions for which a threshold parameter is indicated in Table 16.57. If you do not specify a threshold parameter, the threshold is set to 0.
For the threeparameter Weibull distribution described in Table 16.57, the scale, shape, and threshold parameters are estimated by maximum likelihood.
You should not interpret the parameters and as representing the means and standard deviations for all of the distributions in Table 16.57. The normal is the only distribution in Table 16.57 for which this is the case.
Table 16.57: Distributions and Parameters for PROBPLOT and RELATIONPLOT Statements
Parameters 


Distribution 
Density Function 
Location 
Scale 
Shape 
Threshold 
Normal 




Lognormal 





Lognormal 





(base 10) 

Extreme value 




Weibull 





Exponential 




Logistic 




Loglogistic 





Threeparameter Weibull 




The exponential distribution shown in Table 16.57 is a special case of the Weibull distribution with . The remaining distributions in Table 16.57 are related to one another as shown in Table 16.58. The threshold parameter, , is assumed to be 0 in Table 16.58.
Table 16.58: Relationship among Life Distributions
Distribution of T 
Parameters 
Distribution of Y=T 
Parameters 


Lognormal 


Normal 


Weibull 


Extreme value 


Loglogistic 


Logistic 


All of the distributions in Table 16.57 except the threeparameter Weibull are available for regression model estimation by using the MODEL statement. In addition, you can fit regression models with the generalized gamma distribution with the following probability density function :

If a lifetime T has the generalized gamma distribution, then the logarithm of the lifetime has the generalized loggamma distribution, with the following probability density function . When the gamma distribution is specified, the logarithms of the lifetimes are used as responses, and the generalized loggamma distribution is used to estimate the parameters by maximum likelihood.

See Lawless (2003) and Meeker and Escobar (1998) for a description of the generalized gamma and generalized loggamma distributions.
When , the generalized loggamma distribution reduces to the extreme value distribution with parameters and . In this case, the log lifetimes have the extreme value distribution, or, equivalently, the lifetimes have the Weibull distribution with parameters and . When , the generalized loggamma reduces to the normal distribution with parameters and . In this case, the (unlogged) lifetimes have the lognormal distribution with parameters and . This chapter uses the notation for the location, for the scale, and for the shape parameters for the generalized loggamma distribution.
You can use the ANALYZE statement to compute parameter estimates and other statistics for the distributions in Table 16.57. In addition, you can compute estimates for the binomial and Poisson distributions. The forms of these distributions are shown in Table 16.59.
Table 16.59: Binomial and Poisson Distributions
Distribution 
Pr{Y=y} 
Parameter 
Parameter Name 

Binomial 

p 
binomial probability 
Poisson 


Poisson mean 