Hi @ScatterMarten13,
Welcome in the Community !
I'm not sure to understand if you already have a dataset ready for the analysis, or if you want to plan your experiments ?
- In the first case, depending on the data quality and representativeness, you may or may not be able to identify some relationships. Analyzing the quality of your data collection before any modeling (evaluate sample size & power, correlations among variables, multicollinearity, measurement repeatability & reproducibility, etc...) will be key to understand the outputs from your model and determine how much trust you can put in your model. Then, using Fit Model could help analyze the results and evaluate the relevance of the model, both statistically and with domain expertise.
- In the second case, you can create a design according to your needs, to investigate all main effects and the 2- and 3-factors interactions. You can create the design using the platform MSA Designs (since Mice and Experimenters could be interpreted respectively as "Gauge" and "Operator" factors) or more generally using the Custom Designs platform. The analysis is then a lot easier as the data collection is linked to your objective and the assumed model you have specified.
In each case, doing validation experiments to confirm your interpretations and conclusions will help confirming your hypothesis.
I hope this first discussion starter may help you,
Victor GUILLER
Data & Analytics