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I think I might need the help of this valuable community for a task. I have been given a dataset for 100 numerical independent variables (IVs) that predict output for 200 numerical values (from monte carlo simulation results). Which statistical technique should I start exploring and trying on my dataset? The number of observations can be increased for more points to enhance learning an algorithm. From this, I would like to learn a few insights, such as the multiple collinearity among IVs, their overall contribution to dependent variables.

Thank you very much.

UKadir
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  • do you have to predict these 200 dependent variables all at once? Or can they be separated in independent models? – Leevo Oct 23 '19 at 14:32
  • Hello Leevo, we're exploring both of these options. If we're ok with separation into 200 individual models, then we'll fit 200 separate models, I guess. Right? If we\re not ok, i.e., predict all of them at once, how should we proceed? Thanks again. – UKadir Oct 24 '19 at 16:42
  • Are these variables you want to predict dependent on each other? – Leevo Oct 25 '19 at 06:54
  • What would be the best way to figure out the dependence/independence among these variables? – UKadir Nov 05 '19 at 13:40

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