See how to compare and contrast situational usefulness of two methods for looking at lots of process variables at the same time: Model Driven Multivariate Control Charts (MDMCC) and Process Screening.
Another more "statistical approach" is to use the Durbin Watson Test:
Among the many sampling approaches include: Systematic, Stratified, Cluster, and Multi-stage.
I've tried generating Column Formulas in JMP using JSL to mimic each of these sampling strategies:
Consider a random sample of 1000 test data with a sample of size 250 for each of 4 Lots.
Systematic Sample:
If(Sequence(1, 5) == 5, 1, 0)
Stratified Sample:
If( Col Shuffle( :Lot ) <= 50,
1,
0
);
Cluster Sample Select Lots 1 or Lot 3:
If( :Lot == 1 | :Lot == 3,
1,
0
)
Random Sample Cluster Lots 1 or Lot 3:
If(
:Cluster Select Lot 1 or Lot 3 == 1 & If( Col Shuffle( :Lot ) <= 50,
1,
0
),
1,
0
)
Multi-stage Sample n of 50 from Lot 1 only:
If( If( :Lot == 1, 1, 0 ) == 1 & If( Col Shuffle( :Lot ) <= 50, 1, 0 ),
1,
0
)
Multi-stage Sample of n = 41 + n = 8 for a total sample size of 49 from Lot 1 only:
Col 1 =
If( :"Multistage Sample 1 n=50"n == 1,
0,
If( If( :Lot == 1, 1, 0 ) == 1 & If( Col Shuffle( :Lot ) <= 50, 1, 0 ),
1,
0
)
)
Col 2 =
If( :"Multistage Sample 2 n=50 '"n == 1,
0,
If(
If( :Lot == 1, 1, 0 ) == 1 & If(
Col Shuffle( :Lot ) <= 50 - Col Sum( :"Multistage Sample 2 n=50 '"n ),
1,
0
),
1,
0
)
)
@Byron_JMP @Ben_Sagalovsky A thought from August 20 live webinar: I am curious about the role of autocorrelation for Principal Components calculations, etc. And associated with this, the issue of subsampling process data and deciding on what is the most appropriate interval to sample, and how to determine it.
