The SHEWHART Procedure

Calculating the Chart Statistic

In this situation, it was shown by Gnanadesikan and Kettenring (1972), using a result of Wilks (1962), that $T^2_ i$ is exactly distributed as a multiple of a variable with a beta distribution. Specifically,

\[ T^2_ i \sim \frac{(n-1)^2}{n}~ B \left( \frac{p}{2}, \frac{n-p-1}{2} \right) \]

Tracy, Young, and Mason (1992) used this result to derive initial control limits for a multivariate chart based on three quality measures from a chemical process in the start-up phase: percent of impurities, temperature, and concentration. The remainder of this section describes the construction of a multivariate control chart using their data, which are given here by the data set Startup. 

data Startup;
   input Sample Impure Temp Conc;
   label Sample = 'Sample Number'
         Impure = 'Impurities'
         Temp   = 'Temperature'
         Conc   = 'Concentration' ;
   datalines;
 1  14.92  85.77  42.26
 2  16.90  83.77  43.44
 3  17.38  84.46  42.74
 4  16.90  86.27  43.60
 5  16.92  85.23  43.18
 6  16.71  83.81  43.72
 7  17.07  86.08  43.33
 8  16.93  85.85  43.41
 9  16.71  85.73  43.28
10  16.88  86.27  42.59
11  16.73  83.46  44.00
12  17.07  85.81  42.78
13  17.60  85.92  43.11
14  16.90  84.23  43.48
;

In preparation for the computation of the control limits, the sample size is calculated and parameter variables are defined. 

proc means data=Startup noprint ;
   var Impure Temp Conc;
   output out=means n=n;
run;

data Startup;
   if _n_ = 1 then set means;
   set Startup;
   p        = 3;
   _subn_   = 1;
   _limitn_ = 1;
run;

Next, the PRINCOMP procedure is used to compute the principal components of the variables and save them in an output data set named Prin. 

proc princomp data=Startup out=Prin outstat=scores std cov;
   var Impure Temp Conc;
run;

The following statements compute $T^2_ i$ and its exact control limits, using the fact that $T^2_ i$ is the sum of squares of the principal components.[57] Note that these statements create several special SAS variables so that the data set Prin can subsequently be read as a TABLE= input data set by the SHEWHART procedure. These special variables begin and end with an underscore character. The data set Prin is listed in Figure 18.222. 

data Prin (rename=(tsquare=_subx_));
   length _var_ $ 8 ;
   drop prin1 prin2 prin3 _type_ _freq_;
   set Prin;
   comp1   = prin1*prin1;
   comp2   = prin2*prin2;
   comp3   = prin3*prin3;
   tsquare = comp1 + comp2 + comp3;
   _var_   = 'tsquare';
   _alpha_ = 0.05;
   _lclx_  = ((n-1)*(n-1)/n)*betainv(_alpha_/2, p/2, (n-p-1)/2);
   _mean_  = ((n-1)*(n-1)/n)*betainv(0.5, p/2, (n-p-1)/2);
   _uclx_  = ((n-1)*(n-1)/n)*betainv(1-_alpha_/2, p/2, (n-p-1)/2);
   label tsquare = 'T Squared'
         comp1   = 'Comp 1'
         comp2   = 'Comp 2'
         comp3   = 'Comp 3';
run;

Figure 18.222: The Data Set Prin

T2 Chart For Chemical Example

_var_ n Sample Impure Temp Conc p _subn_ _limitn_ comp1 comp2 comp3 _subx_ _alpha_ _lclx_ _mean_ _uclx_
tsquare 14 1 14.92 85.77 42.26 3 1 1 0.79603 10.1137 0.01606 10.9257 0.05 0.24604 2.44144 7.13966
tsquare 14 2 16.90 83.77 43.44 3 1 1 1.84804 0.0162 0.17681 2.0410 0.05 0.24604 2.44144 7.13966
tsquare 14 3 17.38 84.46 42.74 3 1 1 0.33397 0.1538 5.09491 5.5827 0.05 0.24604 2.44144 7.13966
tsquare 14 4 16.90 86.27 43.60 3 1 1 0.77286 0.3289 2.76215 3.8640 0.05 0.24604 2.44144 7.13966
tsquare 14 5 16.92 85.23 43.18 3 1 1 0.00147 0.0165 0.01919 0.0372 0.05 0.24604 2.44144 7.13966
tsquare 14 6 16.71 83.81 43.72 3 1 1 1.91534 0.0645 0.27362 2.2534 0.05 0.24604 2.44144 7.13966
tsquare 14 7 17.07 86.08 43.33 3 1 1 0.58596 0.4079 0.44146 1.4354 0.05 0.24604 2.44144 7.13966
tsquare 14 8 16.93 85.85 43.41 3 1 1 0.29543 0.1729 0.73939 1.2077 0.05 0.24604 2.44144 7.13966
tsquare 14 9 16.71 85.73 43.28 3 1 1 0.23166 0.0001 0.44483 0.6766 0.05 0.24604 2.44144 7.13966
tsquare 14 10 16.88 86.27 42.59 3 1 1 1.30518 0.0004 0.86364 2.1692 0.05 0.24604 2.44144 7.13966
tsquare 14 11 16.73 83.46 44.00 3 1 1 3.15791 0.0274 0.98639 4.1717 0.05 0.24604 2.44144 7.13966
tsquare 14 12 17.07 85.81 42.78 3 1 1 0.43819 0.0823 0.87976 1.4003 0.05 0.24604 2.44144 7.13966
tsquare 14 13 17.60 85.92 43.11 3 1 1 0.41494 1.6153 0.30167 2.3320 0.05 0.24604 2.44144 7.13966
tsquare 14 14 16.90 84.23 43.48 3 1 1 0.90302 0.0001 0.00010 0.9032 0.05 0.24604 2.44144 7.13966 




            

            


            

               
            

            
You can now use the data set Prin as input to the SHEWHART procedure to create the multivariate control chart displayed in Figure 18.223. 
            


ods graphics off;
title 'T' m=(+0,+0.5) '2'
          m=(+0,-0.5) ' Chart For Chemical Example';
proc shewhart table=Prin;
   xchart tsquare*Sample /
      xsymbol  = mu
      nolegend ;
run;



  

            

               
Figure 18.223: Multivariate Control Chart for Chemical Process

               

                  
Multivariate Control Chart for Chemical Process

               

            

            


            

               The methods used in this example easily generalize to other types of multivariate control charts. You can create charts using
               the $\chi ^2$ and F distributions by using the appropriate CINV or FINV function in place of the BETAINV function
               . For details, refer to Alt (1985), Jackson (1980, 1991), and Ryan (1989). 
            

            





                  
[57] Refer to Jackson (1980).