Note: See OC Curve for a p Chart in the SAS/QC Sample Library.
This example uses the GPLOT procedure and the OUTLIMITS= data set Faillim2
from the previous example to plot an OC curve for the p chart shown in Output 18.25.3.
The OC curve displays (the probability that lies within the control limits) as a function of p (the true proportion nonconforming). The computations are exact, assuming that the process is in control and that the number of nonconforming items () has a binomial distribution.
The value of is computed as follows:
Here, denotes the incomplete beta function. The following DATA step computes (the variable BETA) as a function of p (the variable p
):
data ocpchart; set Faillim2; keep beta fraction _lclp_ _p_ _uclp_; nucl=_limitn_*_uclp_; nlcl=_limitn_*_lclp_; do p=0 to 500; fraction=p/1000; if nucl=floor(nucl) then adjust=probbnml(fraction,_limitn_,nucl) - probbnml(fraction,_limitn_,nucl-1); else adjust=0; if nlcl=0 then beta=1 - probbeta(fraction,nucl,_limitn_-nucl+1) + adjust; else beta=probbeta(fraction,nlcl,_limitn_-nlcl+1) - probbeta(fraction,nucl,_limitn_-nucl+1) + adjust; if beta >= 0.001 then output; end; call symput('lcl', put(_lclp_,5.3)); call symput('mean',put(_p_, 5.3)); call symput('ucl', put(_uclp_,5.3)); run;
The following statements display the OC curve shown in Output 18.26.1:
proc sgplot data=ocpchart; series x=fraction y=beta / lineattrs=(thickness=2); xaxis values=(0 to 0.06 by 0.005) grid; yaxis grid; label fraction = 'Fraction Nonconforming' beta = 'Beta'; run;
Output 18.26.1: OC Curve for p Chart