Hi @blip555555,
Welcome in the Community !
Random and fixed effects play a different role in the analysis and in your model. Fixed effects have an impact on mean response (intercept), whereas random effects have an impact on random error (variance). You can read this section to learn more about Random Effect Models. and Example of Estimating Random Effect Parameters (jmp.com)
The variation explained by all your effects (fixed + random) can be found with the R²/R² adjusted values of your model.
The type of factor (random vs. fixed) is decided before the experiments, depending on the goal of the analysis, the assumptions about the levels representativity of this factor and the inference space, and the physical/experimental possibility to change them in a reproducible way. You can read more in closely related disccusions :
Prediction equation for randomly chosen factors
Random vs Fixed Blocking Factor in DOE
Random Effect vs Fixed Effects influence on Total model Rsq
Hope this response will help you,
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)