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Nice. I see you cover hierarchical (multilevel) linear models, which is already a big step up from the techniques normally covered in STATS101. The general adv
by ivansavz 2y ago
Nice. I see you cover hierarchical (multilevel) linear models, which is already a big step up from the techniques normally covered in STATS101.
The general advice about measuring/comparing models also seems useful.
- mnky9800n 2y agoYeah I tried to make the point that even in the case of multi level models you still should consider their ability to predict because otherwise how can you trust the model understands the underlying correlation structure of your data? That’s because many people had been advocating for these models dogmatically while presenting very poor fit statistics (r^2<0.2) while making big claims. Since I finished my phd I calmed down a bit haha. Now I just run workshops and conferences instead. And I try to present statistics and machine learning as building a lab apparatus. Once the model is built then you can ask it research questions. But simply building the model is not research.