Not sure what kind of experiment you ran? Component A is certainly biased to level 1. What model were you using to design this experiment? B&C make a 3^2, but A is not orthogonal? How were the 10 data points for each Y gotten for each treatment? Are those repeats? Are they in time series?
There are a number of outliers also detected. Perhaps you need to investigate those and then decide what to do with those data points. Once you have considered that, you will use the 10 data points per treatment to calculate a slope (rise/run) for each Y. Analyze the slopes as the Y in fit model.
I'd start by looking at the data. Also make sure the variation ids of practical significance before doing any quantitative analysis. I added some simple scripts to your data table (Multivariate, and graph builder by factor)
"All models are wrong, some are useful" G.E.P. Box