The UNIVARIATE Procedure 
Distributions for Probability and QQ Plots 
You can use the PROBPLOT and QQPLOT statements to request probability and QQ plots that are based on the theoretical distributions summarized in Table 4.86.
Parameters 


Distribution 
Density Function 
Range 
Location 
Scale 
Shape 
beta 




, 
exponential 





gamma 





lognormal 





(3parameter) 

normal 

all 



Weibull 





(3parameter) 

Weibull 





(2parameter) 
(known) 
You can request these distributions with the BETA, EXPONENTIAL, GAMMA, LOGNORMAL, NORMAL, WEIBULL, and WEIBULL2 options, respectively. If you do not specify a distribution option, a normal probability plot or a normal QQ plot is created.
The following sections provide details for constructing QQ plots that are based on these distributions. Probability plots are constructed similarly except that the horizontal axis is scaled in percentile units.
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the inverse normalized incomplete beta function, is the number of nonmissing observations, and and are the shape parameters of the beta distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for ALPHA= and BETA= tends to be linear with intercept and slope if the data are beta distributed with the specific density function
where and
lower threshold parameter
scale parameter
first shape parameter
second shape parameter
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot tends to be linear with intercept and slope if the data are exponentially distributed with the specific density function
where is a threshold parameter, and is a positive scale parameter.
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the inverse normalized incomplete gamma function, is the number of nonmissing observations, and is the shape parameter of the gamma distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for ALPHA= tends to be linear with intercept and slope if the data are gamma distributed with the specific density function
where
threshold parameter
scale parameter
shape parameter
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the inverse cumulative standard normal distribution, is the number of nonmissing observations, and is the shape parameter of the lognormal distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for SIGMA= tends to be linear with intercept and slope if the data are lognormally distributed with the specific density function
where
threshold parameter
scale parameter
shape parameter
See Example 4.26 and Example 4.33.
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the inverse cumulative standard normal distribution and is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
The point pattern on the plot tends to be linear with intercept and slope if the data are normally distributed with the specific density function
where is the mean and is the standard deviation ().
To create the plot, the observations are ordered from smallest to largest, and the th ordered observation is plotted against the quantile , where is the number of nonmissing observations, and is the Weibull distribution shape parameter. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for C= tends to be linear with intercept and slope if the data are Weibull distributed with the specific density function
where
threshold parameter
scale parameter
shape parameter
See Example 4.34.
To create the plot, the observations are ordered from smallest to largest, and the log of the shifted th ordered observation , denoted by , is plotted against the quantile , where is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
Unlike the threeparameter Weibull quantile, the preceding expression is free of distribution parameters. Consequently, the C= shape parameter is not mandatory with the WEIBULL2 distribution option.
The pattern on the plot for THETA= tends to be linear with intercept and slope if the data are Weibull distributed with the specific density function
where
known lower threshold
scale parameter
shape parameter
See Example 4.34.
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