You can use QQ plots to estimate shape, location, and scale parameters and to estimate percentiles. If you are working with a normal QQ plot, you can also estimate certain capability indices.
Some distribution options in the QQPLOT statement require that you specify one or two shape parameters in parentheses after the distribution keyword. These are summarized in Table 5.72.
You can visually estimate a shape parameter by specifying a list of values for the shape parameter option. A separate plot is displayed for each value, and you can then select the value that linearizes the point pattern. Alternatively, you can request that the plot be created using an estimated shape parameter. See the entries for the distribution options in the section Dictionary of Options. for details on specification of shape parameters. Example 5.22 and Example 5.23 illustrate shape parameter estimation with lognormal and Weibull QQ plots.
Note that for QQ plots requested with the WEIBULL2 option, you can estimate the shape parameter c from a linear pattern using the fact that the slope of the pattern is . For an illustration, see Example 5.23.
Table 5.72: Shape Parameter Options for the QQPLOT Statement
Distribution Keyword 
Mandatory Shape Parameter Option 
Range 

BETA 
ALPHA=, BETA= 
, 
EXPONENTIAL 
None 

GAMMA 
ALPHA= 

GUMBEL 
None 

LOGNORMAL 
SIGMA= 

NORMAL 
None 

PARETO 
ALPHA= 

POWER 
ALPHA= 

RAYLEIGH 
None 

WEIBULL 
C=c 
a > 0 
WEIBULL2 
None 
When the point pattern on a QQ plot is linear, its intercept and slope provide estimates of the location and scale parameters. (An exception to this rule is the twoparameter Weibull distribution, for which the intercept and slope are related to the scale and shape parameters.) Table 5.73 shows how the intercept and slope are related to the parameters for each distribution supported by the QQPLOT statement.
Table 5.73: Intercept and Slope of Linear QQ Plots
Parameters 
Linear Pattern 


Distribution 
Location 
Scale 
Shape 
Intercept 
Slope 
Beta 


, 


Exponential 





Gamma 





Gumbel 





Lognormal 





Normal 





Generalized Pareto 





Power Function 





Rayleigh 





Weibull (3parameter) 


c 


Weibull (2parameter) 
(known) 

c 


You can enhance a QQ plot with a diagonal distribution reference line by specifying the parameters that determine the slope and intercept of the line; alternatively, you can request estimates for these parameters. This line is an aid to checking the linearity of the point pattern, and it facilitates parameter estimation. For instance, specifying MU=3 and SIGMA=2 with the NORMAL option requests a line with intercept 3 and slope 2. Specifying SIGMA=1 and C=2 with the WEIBULL2 option requests a line with intercept and slope .
With the LOGNORMAL and WEIBULL2 options, you can specify the slope directly with the SLOPE= option. That is, for the LOGNORMAL option, specifying THETA= and SLOPE= gives the same reference line as specifying THETA= and ZETA=. For the WEIBULL2 option, specifying SIGMA= and SLOPE= gives the same reference line as specifying SIGMA= and C=.
For an example of parameter estimation using a normal QQ plot, see Adding a Distribution Reference Line. Example 5.22 illustrates parameter estimation using a lognormal plot, and Example 5.23 illustrates estimation using twoparameter and threeparameter Weibull plots.
There are two ways to estimate percentiles from a QQ plot:
Specify the PCTLAXIS option, which adds a percentile axis opposite the theoretical quantile axis. The scale for the percentile axis ranges between 0 and 100 with tick marks at percentile values such as 1, 5, 10, 25, 50, 75, 90, 95, and 99. See Figure 5.41 for an example.
Specify the PCTLSCALE option, which relabels the horizontal axis tick marks with their percentile equivalents but does not alter their spacing. For example, on a normal QQ plot, the tick mark labeled “0” is relabeled as “50” since the 50th percentile corresponds to the zero quantile. See Figure 5.42 for an example.
You can also estimate percentiles using probability plots created with the PROBPLOT statement. See Output 5.20.1 for an example.
When the point pattern on a normal QQ plot is linear, you can estimate the capability indices , , and from the plot, as explained by Rodriguez (1992). This method exploits the fact that the horizontal axis of a QQ plot indicates the distance in standard deviation units (multiple of ) between a measurement or specification limit and the process average.
In particular, onethird the standardized distance between an upper specification limit and the mean is the onesided capability index CPU.

Likewise, onethird the standardized distance between a lower specification limit and the mean is the onesided capability index CPL.

Consequently, if you rescale the quantile axis of a normal QQ plot by a factor of three, you can read CPU and CPL from the horizontal coordinates of the points at which the upper and lower specification lines intersect the point pattern. Since is defined as the minimum of CPU and CPL, this method also provides a graphical estimate of . For an illustration, see Example 5.24.