You have given us no information with which to help you.
I examined your design. It is a small design (12 runs) so it will likely have low power. It will find real effects only if the effects are very large compared to the random effect of the responses (4-fold to 5-fold larger). Is that result what you expect? Your factor ranges should be wide to produce a large effect. Is that what you did? It is a super-saturated design. You have 10 terms with estimability set to if possible (all possible two-factor interactions). You have 8 terms that are necessary to estimate (include a three-factor interaction). You do not have any terms to account for a potential non-linear response to changing these factors. This appears to be a factor screening experiment. Is that your purpose?
I examined your analysis. You saved an analysis in which you attempted to estimate all 18 terms with 12 runs, so you have many singularities. Is that analysis what you intended? The methodology for the analysis of super-saturated designs would not recommend such an analysis.
I used stepwise regression for SLOPE. I manually chose terms to add, one term at a time. I arrived at this tentative model.
Notice the large uncertainty in the model prediction. Your CV for SLOPE is about 23%. The largest, most significant effects, are 2 two-factor interactions. Is that reasonable? Does that surprise you?
I found no significant effects for LOD. Does that surprise you? Do you expect the LOD to be normally distributed as expected by this modeling method?
I found PVC:TCP to be the only significant effect for LOQ. Does that surprise you?
I found no significant effects for R.
I found the same effects for RESPONSE TIME as I did for SLOPE.
Other methods are available to select different models in such a case if these models are unsatisfactory. It is obvious that any of the models that you select for this data would need to be empirically confirmed.
thank you very much for your help
1- Actually, i have a little experiance with JMP and DOE
2- I designed this model in order to inspect the main effects and ( any interaction if possible) of the three continuous factors PT,PVC:TCP and MT and one categorical factor ionophore of three levels (CX-BCD-none) on the mentioned five responses (slope,LOD,LOQ,R and RT)
3-the designed model is to reach the optimum factors combinationfor max desirability.
4- i set values of 0.6,0.05, 0.15, 0.05, 0.15 importance for the responses slope , LOD,LOQ,r and RT
5- is this model truly designed? and how can i usefully analyze this data especially i already did the twelve experiments??
6- really, i did not use a wide range for each factor.
finally i need your in details of using this model to examine
1- the main effects of each factor
2- interaction terms
3- prediction formulae for each response
4- optimum factos combination for max desirability
Your four specific requests at the end of your reply can be answered quite well by the excellent documentation. Please see Help > Books > Design of Experiments Guide. This guide contains both general and specific information about the design and analysis of a variety of experiments. It illustrates each aspect with examples. The sample data sets are provided so that you can practice before analyzing your own data.
(Note that there is another guide for Fitting Linear Models that covers the details about the kind of model that you are using.)
I just examined your data table again and found it quite messed up. That is, some of the meta-data about the factors and responses was inconsistent or wrong and these problems affect the modeling and inference. That result should not happen if you design the experiment with JMP. You should not change these properties unless you know what you are doing and have a good reason.
I fixed the problems. I re-ran my tentative analyses.* I saved the prediction formula for each tentative model. I profiled these models. I had to exclude LOQ because it never achieved the desired level.
I saved scripts for each step to the data table. The updated data table is attached.
* I manually selected tentative models using the stepwise platform and a generous alpha level of 0.15 to hopefully increase the low power, at the risk that some of these effects will not be confirmed.
There are several DOE planning and analysis oriented On Demand and Live webinars available at no charge on jmp.com. These videos are largely focused on 'how to' in JMP...and NOT focused on teaching someone with little knowledge of the DOE problem solving process. So if you are someone looking for a 'how to' in JMP...then I suggest perusing the many offerings scattered around the Mastering JMP webinar series linked here:
If you are looking for a more comprehensive learning experience that teaches the DOE problem solving process AND 'how to' in JMP, SAS offers a variety of training experiences as well. You can peruse these offerings here: