Analysis of Opt DOE simulation results

mandeson91

Dear JMPer, 

 

Currently, I'm under analysis a Opt DOE simulation results. However, I don't get consistent result form simulations and also get to different trends during counter profile analysis. I wonder if any methodology or analysis could use to understand which simulation result is valid. 

 Here is my simulation process. The DOE matrix is in Table 1

1. Fit model (Simulation 1):
   1. Put 4 inputs in Simulation 1 and select surface response mode in macro functions.
   2. Remove interaction option from same factor, eg input A * input A
   3. Run the simulation with standard least squares and effect leverage.
   4. Table 2 shows Rsquare of summary of fit and Output trend based on Input A/C (Input A/C is key factors for the process)
2. Fit two level screening (Simulation 2):  
   
   1. Select Output A to run fit two level screening.
   2. Use defined important factors, such as output's interaction and individual outputs. 
   3. Run the simulation with standard least squares and effect leverage. 
   4. Table 2 shows Rsquare of Summary Of Fit and Output trend based on Input A/C (Input A/C is key factors for the process)

Output B and Output C is able to predict the trend based on higher input or lower input (due to physic). However, Output A is the important response that I would like to see what simulation would show the trend. Based on Rsquare of Summary Of Fit, Simulation 1 seems batter than Simulation 2. However, output trend of Simulation 2 is more logical to me than Simulation 1. Therefore, I wonder if anyone could share some input to have better analysis for both simulation results. Please kindly share me your inputs. Much appreciate your time and help. 

 

Thank you, 

 

Bests,
Andy

 

 

Table 1. DOE matrix

Leg#	Pattern	Input A	Input B	Input C	Input D	Input E	Input F	Output A	Output B	Output C
1	+−−++−	300	200	100	3	25	1	0.0%	2,463	61
2	−+−+−+	200	400	100	3	15	5	0.0%	3,572	45
3	++−−++	300	400	100	1	25	5	0.0%	3,990	47
4	−−−−−−	200	200	100	1	15	1	0.0%	3,245	31
5	+−−−−+	300	200	100	1	15	5	0.0%	3,017	34
6	−−−+++	200	200	100	3	25	5	0.0%	3,842	76
7	−+−−+−	200	400	100	1	25	1	0.0%	3,431	48
8	++−+−−	300	400	100	3	15	1	0.0%	2,409	51
9	000000	250	300	110	2	20	3	0.0%	3,336	66
10	000000	250	300	110	2	20	3	0.0%	2,564	66
11	−++++−	200	400	120	3	25	1	0.0%	3,289	62
12	−−+−++	200	200	120	1	25	5	0.0%	2,698	53
13	+−++−+	300	200	120	3	15	5	0.0%	3,017	47
14	+++−−−	300	400	120	1	15	1	0.0%	3,460	31
15	−++−−+	200	400	120	1	15	5	2.8%	3,503	28
16	−−++−−	200	200	120	3	15	1	0.0%	2,835	42
17	+−+−+−	300	200	120	1	25	1	0.0%	2,304	42
18	++++++	300	400	120	3	25	5	0.0%	3,431	69
19	POR	150	300	120	0	0	0	7.1%	3,642	16
20	POR	150	300	120	0	0	0	8.3%	3,093	15

 

Table 2. Simulation 1 and Simulation 2 Results. 

JMP analysis	Simulation 1	Simulation 1	Simulation 1	Simulation 2	Simulation 2	Simulation 2
Matrix	Output A	Output B	Output C	Output A	Output B	Output C
Input A high	Reduce	reduce	two trends	increase	Increase	reduce
Input A low	increase	increase	two trends	reduce	Reduce	Increase
Input C high	Increase	Increase	reduce	reduce	Increase	Reduce
Input C low	reduce	reduce	increase	increase	reduce	increase
Summary of fit Rsquare	0.96	0.78	0.84	0.95	0.59	0.85

Mark_Bailey

You asked, "Based on your experience, should the simulation be focused on main effects which has defined by fit two level screening?"

 

First of all, this is NOT a simulation. It is a regression analysis to fit a linear model.

 

There are several common ways to approach the analysis of an experiment based on a fractional factorial design. You can approach it, as I said, using two stages in which you first select the main effects, believing that the principles of hierarchy holds, and then, add interaction terms for the active factors, believing that the principle of heredity holds. This approach is often a successful way to select the model. This model, like all others, must be verified with new empirical observations (augmented runs).

 

You asked, "Could you share me script for stepwise platform simulation?"

 

You do not need a script. Start your analysis with Fit Model as usual. Specify the response data column in the Y role. Specify the terms in the linear predictor for the Effects. But switch the Personality from Standard Least Squares to Stepwise..

 

Please see JMP > Books > Fitting Linear Models > Stepwise chapter.