Dear statman,
Thank you very much for your time and your answer, I will try to reply below:
Welcome to the community!
Thank you very much
There are a number of questions/comments relating to your situation in addition to the ones already posed by Mark. Here are some of my thoughts:
1. Is this a screening design or are you further down the knowledge continuum (is this the first experiment in a series or have you already run some experiments). What questions are you trying to answer? Have you predicted or anticipated the potential results of this experiment? How will the information learned in this experiment added to your knowledge? What will be your next set of work? It is extremely difficult to give sound advice without understanding the situation better.
I will try to answer the best I can:
It is set of experiment which is based on previous knwoledge. The objective is to optimize 3 responses, all are continous. No I can not anticipate the potential results at the moment. I want to reach some threshold for my responses, once this threshold are reached, the higher it is, the best it is.
2. Blocking is a strategy to increase the inference space while either improving the precision of the design or at least not compromising the precision. It accomplishes this by confounding noise (factors you are not willing to manage) with the block effect. The noise within block remains constant (thereby having no effect and reducing the within block random errors due to noise, thus increasing the precision of the design and lowering the MSE) then that noise is purposely changed between blocks (so as to increase the inference space). In addition, if you are able to assign the factors confounded with the block, you may be able to estimate block-by-design factor interactions to determine the robustness of your design factors to noise (absence of noise-by-factor interactions). Is this what you intend to do?
If I have well understood, this is correct. Yes I am able to assign the factor confounded with the block but please note that this factor is not completly confounded. This factor is a 2 level categorical factor and I can have up to 6 or more blocs.
3. You have 5 design factors (4 with 2-levels and 1 with 3 levels)? It appears you are fractionating the design or attempting some optimal design strategy and then considering the block. This is likely an incomplete block (BIB). There are alternatives...as Mark suggests, if the noise is measurable, you could trade the noise as a covariate.
Yes that's correct. I am not used to the use of covariate, I will have a look, thanks for the tips.
4. What is A? Is A "nested" within material (different levels of A are used conditional on Material)?
A is a categorical factor with 2 levels (starting material is treated or untreated).
5. Why do you not want to quantify the effect of material? "the categorical should be use to evaluate the effect of my starting materials, which I do not really need." Are you concerned about the potential interactions of the design factors with Material?
Yes that's true my factors might interact with my starting material. Unfortunatly my different starting materials are in fact the same "starting material" but with different batches. This batches are and will be very different from each other. My objective is to evaluate the effect of each parameters "independently" of my starting material.
I do not know if my answer is clear.
6. What are the response variables? Are they continuous? What is your predicted rank order of model effects?
3 responses, all continuous. I do not know the rank order of model effect, I would go for rank 1 but I might add the higher, it depends on the number of runs.
Thank you very much.
Have a nice day,
