#### Creating a Normal Probability-Probability Plot

See CAPPP1 in the SAS/QC Sample LibraryThe distances between two holes cut into 50 steel sheets are measured and saved as values of the variable Distance in the following data set: [21]

data Sheets;
input Distance @@;
label Distance='Hole Distance in cm';
datalines;
9.80 10.20 10.27  9.70  9.76
10.11 10.24 10.20 10.24  9.63
9.99  9.78 10.10 10.21 10.00
9.96  9.79 10.08  9.79 10.06
10.10  9.95  9.84 10.11  9.93
10.56 10.47  9.42 10.44 10.16
10.11 10.36  9.94  9.77  9.36
9.89  9.62 10.05  9.72  9.82
9.99 10.16 10.58 10.70  9.54
10.31 10.07 10.33  9.98 10.15
;


The cutting process is in statistical control. As a preliminary step in a capability analysis of the process, it is decided to check whether the distances are normally distributed. The following statements create a P-P plot, shown in Figure 5.31, which is based on the normal distribution with mean and standard deviation :

ods graphics off;
symbol v=plus;
title 'Normal Probability-Probability Plot for Hole Distance';
proc capability data=Sheets noprint;
ppplot Distance / normal(mu=10 sigma=0.3)
square;
run;


The NORMAL option in the PPPLOT statement requests a P-P plot based on the normal cumulative distribution function, and the MU= and SIGMA= normal-options specify and . Note that a P-P plot is always based on a completely specified distribution, in other words, a distribution with specific parameters. In this example, if you did not specify the MU= and SIGMA= normal-options, the sample mean and sample standard deviation would be used for and .

The linearity of the pattern in Figure 5.31 is evidence that the measurements are normally distributed with mean 10 and standard deviation 0.3. The SQUARE option displays the plot in a square format.

[21] These data are also used to create Q-Q plots in QQPLOT Statement: CAPABILITY Procedure.