How can to remove the variation of a factor from data responses in a factorial design?

[#1]

Dear All,

I conducted a factorial experiment. My factors are A (4-levels), B (2-levels) and C (2-levels) in 4 blocks. Factor A is different genotypes of a plant. I measured different traits in this experiment.

I want to put my measurements in a model and I want to remove the variation of factor A from my data response. Can I do it?

I saw a method about it in a paper. They analyzed the response of data (ex. Biomass) to Factor A. Then they saved the residuals and used them as new variables (biomass without the factor A variation). I did the same method in JMP, but I am not sure, is it correct or not?  Is it possible statistically?

Thank you so much

Meelad

[#2]

Dear Phil,

Thank you so much, I am not perfect in statistic and SEM, I am just a beginner. Your advice was useful to me.

Your link that you sent me was helpful. I ran SEM without residuals because I am not sure if I can use them in the model based on my purpose. I think that I need to learn more about residuals. Of course, I found some papers that they used the residuals in the model.

I learnt more things from your advice. 

Thank you so much for your time,

Sincerely yours,

Milad

[#3]

I just want to that the basis for Phil's solution is that a linear model can simultaneously account for all the contributions to each response. You designed a multiple factor experiment but you are analyzing it one factor at a time. It is not an invalid approach, but you can do more with the model that Phil proposed. For example, you can use the Prediction Profiler to visualize the dependence on all the factors.

[#4]

Dear Mark,

Thank you so much for your advice, 

I searched about "Prediction Profiler ". It looks good. I believe that it would be helpful.

Thank you,

Best regards,

Milad