Hello Kanhaiya,

If factor "F" (3 levels) and factor "M" (4 levels) are both between-subjects factors, _and_ you have a control group, then you will likely be best served by treating the data set as having 13 treatments (F1M1, F1M2, F1M3...F3M4, Control) (as in a one-way anova design or the equivalent regression model) and test your specific hypotheses of interest via contrasts.

For example:

Ho: No difference between (al)l treatments & control:

C1: +1, +1, +1, +1, +1, +1, +1, +1, +1, +1, +1, +1, -12

Ho: No difference due to main effect of factor "F":

C2: +1, +1, +1, +1, -1, -1, -1, -1, 0, 0, 0, 0, 0

C3: +2, +2, +2, +2, -1, -1, -1, -1, -1, -1, -1, -1, 0

(Note you need two contrasts to capture all the information association with 3 levels of a factor)

...and so on.

Good luck with your work.