In this situation, it was shown by Gnanadesikan and Kettenring (1972), using a result of Wilks (1962), that is exactly distributed as a multiple of a variable with a beta distribution. Specifically,
|
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 and its exact control limits, using the fact that is the sum of squares of the principal components.[111] 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 17.220.
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 17.220: 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 17.221.
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 17.221: 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 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).